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GitHub - facebookresearch/partnr-planner: A repository accompanying the PARTNR benchmark for using Large Planning Models (LPMs) to solve Human-Robot Collaboration or Robot Instruction Following tasks in the Habitat simulator.
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class=\"heading-element\" dir=\"auto\"\u003ePARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-Agent Tasks\u003c/h1\u003e\u003ca id=\"user-content-partnr-a-benchmark-for-planning-and-reasoning-in-embodied-multi-agent-tasks\" class=\"anchor\" aria-label=\"Permalink: PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-Agent Tasks\" href=\"#partnr-a-benchmark-for-planning-and-reasoning-in-embodied-multi-agent-tasks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis is the official repository for the PARTNR benchmark. It includes instructions for downloading and running the PARTNR benchmark in the Habitat simulator and implements Large Language Model (LLM) based planners as baselines.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://aihabitat.org/partnr/\" rel=\"nofollow\"\u003eProject Website\u003c/a\u003e | \u003ca href=\"https://arxiv.org/pdf/2411.00081\" rel=\"nofollow\"\u003ePaper\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/facebookresearch/partnr-planner/blob/main/docs/cover_fig.png\"\u003e\u003cimg src=\"/facebookresearch/partnr-planner/raw/main/docs/cover_fig.png\" width=\"100%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis codebase contains abstractions for enabling agents to follow free form natural language instructions in the Habitat simulator. The main abstractions are - \u003cstrong\u003eAgent\u003c/strong\u003e, \u003cstrong\u003ePlanner\u003c/strong\u003e, \u003cstrong\u003eTool\u003c/strong\u003e, and \u003cstrong\u003eSkill\u003c/strong\u003e. The agent has access to a set of tools which can allow it to perceive the state of the environment, or interact with the environment using low level skills. This repository also contains the codebase for generating PARTNR datasets using LLMs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCiting PARTNR\u003c/h2\u003e\u003ca id=\"user-content-citing-partnr\" class=\"anchor\" aria-label=\"Permalink: Citing PARTNR\" href=\"#citing-partnr\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you use our codebase or dataset in your research, please cite the \u003ca href=\"https://arxiv.org/pdf/2411.00081\" rel=\"nofollow\"\u003ePARTNR\u003c/a\u003e paper:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@misc{chang2024partnrbenchmarkplanningreasoning,\n title={PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks}, \n author={Matthew Chang and Gunjan Chhablani and Alexander Clegg and Mikael Dallaire Cote and Ruta Desai and Michal Hlavac and Vladimir Karashchuk and Jacob Krantz and Roozbeh Mottaghi and Priyam Parashar and Siddharth Patki and Ishita Prasad and Xavier Puig and Akshara Rai and Ram Ramrakhya and Daniel Tran and Joanne Truong and John M. Turner and Eric Undersander and Tsung-Yen Yang},\n year={2024},\n eprint={2411.00081},\n archivePrefix={arXiv},\n primaryClass={cs.RO},\n url={https://arxiv.org/abs/2411.00081}, \n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@misc{chang2024partnrbenchmarkplanningreasoning,\n title={PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks}, \n author={Matthew Chang and Gunjan Chhablani and Alexander Clegg and Mikael Dallaire Cote and Ruta Desai and Michal Hlavac and Vladimir Karashchuk and Jacob Krantz and Roozbeh Mottaghi and Priyam Parashar and Siddharth Patki and Ishita Prasad and Xavier Puig and Akshara Rai and Ram Ramrakhya and Daniel Tran and Joanne Truong and John M. Turner and Eric Undersander and Tsung-Yen Yang},\n year={2024},\n eprint={2411.00081},\n archivePrefix={arXiv},\n primaryClass={cs.RO},\n url={https://arxiv.org/abs/2411.00081}, \n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCode Organization\u003c/h2\u003e\u003ca id=\"user-content-code-organization\" class=\"anchor\" aria-label=\"Permalink: Code Organization\" href=\"#code-organization\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBelow are the details of various important directories and classes.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003ehabitat-llm\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eAgent\u003c/strong\u003e : Represents the robot or human. An agent can act in the environment.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTools\u003c/strong\u003e : Represents abstractions which enable the agent to perceive or act in the environment.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePlanner\u003c/strong\u003e : Represents centralized and decentralized planners.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLLM\u003c/strong\u003e : Contains abstractions to represent Llama and GPT APIs.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWorldGraph\u003c/strong\u003e : Contains a hierarchical world graph representing rooms, furniture, objects.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePerception\u003c/strong\u003e : Contains a simulated perception pipeline which sends local detections to the world model.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eExamples\u003c/strong\u003e: Contains demos and evaluation programs to show or analyze the performance of planners.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEvaluationRunner\u003c/strong\u003e: Represents an abstraction for running the planners.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eConf\u003c/strong\u003e : Contains hydra config files for all classes.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUtils\u003c/strong\u003e : Contains various utility methods required throughout the codebase.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTests\u003c/strong\u003e : Contains unit tests.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003escripts\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003ehitl_analysis\u003c/strong\u003e : Contains scripts to analyze and replay human-in-the-loop traces.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eprediviz\u003c/strong\u003e : Contains visualization and annotation tools for PARTNR tasks.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInformation Flow\u003c/h2\u003e\u003ca id=\"user-content-information-flow\" class=\"anchor\" aria-label=\"Permalink: Information Flow\" href=\"#information-flow\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBelow is a figure showing the flow of information through decentralized planners in PARTNR. Please note that at there is no explicit communication between the two decentralized planners. The \u003ca href=\"/facebookresearch/partnr-planner/blob/main/habitat_llm/agent/env/environment_interface.py\"\u003eEnvironmentInterface\u003c/a\u003e reads observations for each agent and sends them to the \u003ca href=\"/facebookresearch/partnr-planner/blob/main/habitat_llm/perception/perception_sim.py\"\u003ePerception Module\u003c/a\u003e. The processed observations are used to update the \u003ca href=\"/facebookresearch/partnr-planner/blob/main/habitat_llm/world_model/world_graph.py\"\u003eWorld Graph\u003c/a\u003e and the \u003ca href=\"/facebookresearch/partnr-planner/blob/main/habitat_llm/planner/planner.py\"\u003ePlanner\u003c/a\u003e (based on an LLM or other system) uses the world graph and the task description select a \u003ca href=\"/facebookresearch/partnr-planner/blob/main/habitat_llm/tools/tool.py\"\u003eTool\u003c/a\u003e to interact with the environment.\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/facebookresearch/partnr-planner/blob/main/docs/planner_v3.png\"\u003e\u003cimg src=\"/facebookresearch/partnr-planner/raw/main/docs/planner_v3.png\" width=\"65%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor installation, refer to \u003ca href=\"/facebookresearch/partnr-planner/blob/main/INSTALLATION.md\"\u003eINSTALLATION.md\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuickstart\u003c/h2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-label=\"Permalink: Quickstart\" href=\"#quickstart\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBaselines for a variety of agent configuration are runnable with the provided configurations. Select any of the baselines from \u003ca href=\"/facebookresearch/partnr-planner/blob/main/habitat_llm/conf/baselines\"\u003ehabitat_llm/conf/baselines\u003c/a\u003e. You may need to specify one or two LLMs based on the baseline. Set the inference mode to \u003ccode\u003ehf\u003c/code\u003e and specify the huggingface model to use for inference. Model weights will be downloaded automatically if they are not found. Below are examples using Llama-3.1-8B running on the \u003ccode\u003eval_mini\u003c/code\u003e data split. Replace \u003ccode\u003eval_mini\u003c/code\u003e with one of \u003ccode\u003e[train_2k, val, train, val_mini]\u003c/code\u003e to run other splits.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDecentralized Multi Agent React Summary\u003c/h3\u003e\u003ca id=\"user-content-decentralized-multi-agent-react-summary\" class=\"anchor\" aria-label=\"Permalink: Decentralized Multi Agent React Summary\" href=\"#decentralized-multi-agent-react-summary\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python -m habitat_llm.examples.planner_demo --config-name baselines/decentralized_zero_shot_react_summary.yaml \\\n habitat.dataset.data_path=\u0026quot;data/datasets/partnr_episodes/v0_0/val_mini.json.gz\u0026quot; \\\n evaluation.agents.agent_0.planner.plan_config.llm.inference_mode=hf \\\n evaluation.agents.agent_1.planner.plan_config.llm.inference_mode=hf \\\n evaluation.agents.agent_0.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct \\\n evaluation.agents.agent_1.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct\"\u003e\u003cpre\u003epython -m habitat_llm.examples.planner_demo --config-name baselines/decentralized_zero_shot_react_summary.yaml \\\n habitat.dataset.data_path=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edata/datasets/partnr_episodes/v0_0/val_mini.json.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n evaluation.agents.agent_0.planner.plan_config.llm.inference_mode=hf \\\n evaluation.agents.agent_1.planner.plan_config.llm.inference_mode=hf \\\n evaluation.agents.agent_0.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct \\\n evaluation.agents.agent_1.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCentralized Multi Agent React Summary\u003c/h3\u003e\u003ca id=\"user-content-centralized-multi-agent-react-summary\" class=\"anchor\" aria-label=\"Permalink: Centralized Multi Agent React Summary\" href=\"#centralized-multi-agent-react-summary\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python -m habitat_llm.examples.planner_demo --config-name baselines/centralized_zero_shot_react_summary.yaml \\\n habitat.dataset.data_path=\u0026quot;data/datasets/partnr_episodes/v0_0/val_mini.json.gz\u0026quot; \\\n evaluation.planner.plan_config.llm.inference_mode=hf \\\n evaluation.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct\"\u003e\u003cpre\u003epython -m habitat_llm.examples.planner_demo --config-name baselines/centralized_zero_shot_react_summary.yaml \\\n habitat.dataset.data_path=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edata/datasets/partnr_episodes/v0_0/val_mini.json.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n evaluation.planner.plan_config.llm.inference_mode=hf \\\n evaluation.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSingle Agent React Summary\u003c/h3\u003e\u003ca id=\"user-content-single-agent-react-summary\" class=\"anchor\" aria-label=\"Permalink: Single Agent React Summary\" href=\"#single-agent-react-summary\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python -m habitat_llm.examples.planner_demo --config-name baselines/single_agent_zero_shot_react_summary.yaml \\\n habitat.dataset.data_path=\u0026quot;data/datasets/partnr_episodes/v0_0/val_mini.json.gz\u0026quot; \\\n evaluation.agents.agent_0.planner.plan_config.llm.inference_mode=hf \\\n evaluation.agents.agent_0.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct\"\u003e\u003cpre\u003epython -m habitat_llm.examples.planner_demo --config-name baselines/single_agent_zero_shot_react_summary.yaml \\\n habitat.dataset.data_path=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edata/datasets/partnr_episodes/v0_0/val_mini.json.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n evaluation.agents.agent_0.planner.plan_config.llm.inference_mode=hf \\\n evaluation.agents.agent_0.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHeuristic Planner\u003c/h3\u003e\u003ca id=\"user-content-heuristic-planner\" class=\"anchor\" aria-label=\"Permalink: Heuristic Planner\" href=\"#heuristic-planner\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python -m habitat_llm.examples.planner_demo --config-name baselines/heuristic_full_obs.yaml \\\n habitat.dataset.data_path=\u0026quot;data/datasets/partnr_episodes/v0_0/val_mini.json.gz\u0026quot;\"\u003e\u003cpre\u003epython -m habitat_llm.examples.planner_demo --config-name baselines/heuristic_full_obs.yaml \\\n habitat.dataset.data_path=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edata/datasets/partnr_episodes/v0_0/val_mini.json.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustom Instructions\u003c/h3\u003e\u003ca id=\"user-content-custom-instructions\" class=\"anchor\" aria-label=\"Permalink: Custom Instructions\" href=\"#custom-instructions\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn addition to the PARTNR splits you can run custom instructions as well. Below is an example running a custom instruction using single agent react.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python -m habitat_llm.examples.planner_demo --config-name baselines/single_agent_zero_shot_react_summary.yaml \\\n instruction=\u0026quot;\u0026lt;CUSTOM_INSTRUCTION\u0026gt;\u0026quot; \\\n mode='cli' \\\n evaluation.agents.agent_0.planner.plan_config.llm.inference_mode=hf \\\n evaluation.agents.agent_0.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct\"\u003e\u003cpre\u003epython -m habitat_llm.examples.planner_demo --config-name baselines/single_agent_zero_shot_react_summary.yaml \\\n instruction=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u0026lt;CUSTOM_INSTRUCTION\u0026gt;\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n mode=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003ecli\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e \\\n evaluation.agents.agent_0.planner.plan_config.llm.inference_mode=hf \\\n evaluation.agents.agent_0.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOpenAI Backend\u003c/h3\u003e\u003ca id=\"user-content-openai-backend\" class=\"anchor\" aria-label=\"Permalink: OpenAI Backend\" href=\"#openai-backend\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo run with openai chat as the LLM backed instead set the \u003ccode\u003eplan_config.llm=openai_chat\u003c/code\u003e. Make sure your API keys have been set appropriately.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python -m habitat_llm.examples.planner_demo --config-name baselines/single_agent_zero_shot_react_summary.yaml \\\n habitat.dataset.data_path=\u0026quot;data/datasets/partnr_episodes/v0_0/val_mini.json.gz\u0026quot; \\\n llm@evaluation.agents.agent_0.planner.plan_config.llm=openai_chat\"\u003e\u003cpre\u003epython -m habitat_llm.examples.planner_demo --config-name baselines/single_agent_zero_shot_react_summary.yaml \\\n habitat.dataset.data_path=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edata/datasets/partnr_episodes/v0_0/val_mini.json.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n llm@evaluation.agents.agent_0.planner.plan_config.llm=openai_chat\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNote that constrained generation is not supported for the openai backend.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLearned Skills\u003c/h3\u003e\u003ca id=\"user-content-learned-skills\" class=\"anchor\" aria-label=\"Permalink: Learned Skills\" href=\"#learned-skills\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo run baselines with neural network skills for the robot agent (point nav, pick, and place) append the following argument overrides to the above commands. Please refer to the installation readme to download the skill checkpoints.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\" device=cpu \\\n agent@evaluation.agents.agent_0.config=nn_rearrange_agent_motortoolsonly \\\n habitat_conf/task=rearrange_easy_multi_agent_nn \\\"\u003e\u003cpre\u003e device=cpu \\\n agent@evaluation.agents.agent_0.config=nn_rearrange_agent_motortoolsonly \\\n habitat_conf/task=rearrange_easy_multi_agent_nn \\\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCalculating Results\u003c/h2\u003e\u003ca id=\"user-content-calculating-results\" class=\"anchor\" aria-label=\"Permalink: Calculating Results\" href=\"#calculating-results\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can check the progress of the run and the results so far using\n\u003ccode\u003epython scripts/read_results.py \u0026lt;output_dir\u0026gt;/\u0026lt;dataset_name\u0026gt;\u003c/code\u003e. The default \u003ccode\u003eoutput_dir\u003c/code\u003e is \u003ccode\u003eoutputs/habitat_llm/\u0026lt;timestamp\u0026gt;-\u0026lt;dataset_name\u0026gt;\u003c/code\u003e. This can be overridden using the \u003ccode\u003epaths.results_dir\u003c/code\u003e and \u003ccode\u003eevaluation.output_dir\u003c/code\u003e configuration elements. The output directory also contains the full text traces of the rollouts and other statistics.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTesting episodes\u003c/h2\u003e\u003ca id=\"user-content-testing-episodes\" class=\"anchor\" aria-label=\"Permalink: Testing episodes\" href=\"#testing-episodes\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo test whether a dataset is runnable and the success at step 0, run the following command. Set the \u003ccode\u003ehabitat.dataset.data_path\u003c/code\u003e to the dataset you want to test.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"HYDRA_FULL_ERROR=1 python -m habitat_llm.examples.verify_episodes \\\n --config-name examples/planner_multi_agent_demo_config.yaml \\\n hydra.run.dir=\u0026quot;.\u0026quot; \\\n evaluation=centralized_evaluation_runner_multi_agent \\\n habitat.dataset.data_path=\u0026quot;data/datasets/partnr_episodes/v0_0/val_mini.json.gz\u0026quot; \\\n mode='data' \\\n world_model.partial_obs='False' \\\n evaluation.type=\u0026quot;centralized\u0026quot; \\\n num_proc=5\"\u003e\u003cpre\u003eHYDRA_FULL_ERROR=1 python -m habitat_llm.examples.verify_episodes \\\n --config-name examples/planner_multi_agent_demo_config.yaml \\\n hydra.run.dir=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n evaluation=centralized_evaluation_runner_multi_agent \\\n habitat.dataset.data_path=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edata/datasets/partnr_episodes/v0_0/val_mini.json.gz\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n mode=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003edata\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e \\\n world_model.partial_obs=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003eFalse\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e \\\n evaluation.type=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ecentralized\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n num_proc=5\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSkillRunner: Running Oracle Skills in a Sandbox Environment\u003c/h4\u003e\u003ca id=\"user-content-skillrunner-running-oracle-skills-in-a-sandbox-environment\" class=\"anchor\" aria-label=\"Permalink: SkillRunner: Running Oracle Skills in a Sandbox Environment\" href=\"#skillrunner-running-oracle-skills-in-a-sandbox-environment\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe habitat_llm/examples/skill_runner.py application provides a headless command line interface (CLI) for running custom sequences of oracle skills within a sandbox environment for a given episode.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"HYDRA_FULL_ERROR=1 python -m habitat_llm.examples.skill_runner hydra.run.dir=\u0026quot;.\u0026quot; +skill_runner_show_topdown=True habitat.dataset.data_path=data/datasets/partnr_episodes/v0_0/val_mini.json.gz +skill_runner_episode_id=\u0026quot;334\u0026quot;\"\u003e\u003cpre\u003eHYDRA_FULL_ERROR=1 python -m habitat_llm.examples.skill_runner hydra.run.dir=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e.\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e +skill_runner_show_topdown=True habitat.dataset.data_path=data/datasets/partnr_episodes/v0_0/val_mini.json.gz +skill_runner_episode_id=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e334\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn the above example, episode \u003ccode\u003e334\u003c/code\u003e from the \u003ccode\u003eval_mini.json.gz\u003c/code\u003e dataset will be instantiated, the list of all active Entities (Rooms, Objects, Furniture, and Receptacles) will be printed to the console.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eBy providing CLI option \u003ccode\u003e+skill_runner_show_topdown=True\u003c/code\u003e, the program will render and display a top-down view of the scene upon initialization.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eCLI option \u003ccode\u003e+skill_runner_episode_index=\u003c/code\u003e can be provided instead of \u003ccode\u003e+skill_runner_episode_id=\u003c/code\u003e, in which case the integer index will be used to query the episode from the dataset.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eNote that all environment config values can be overridden from the CLI and defaults are set in \u003ccode\u003ehabitat_llm/conf/examples/skill_runner_default_config.yaml\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eOnce the Episode is initialized, the user will be presented with a list of available skills (e.g. Navigate, Pick, Place, Open, Close) and prompted to enter a command.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCommands can take the form of a skill call, for example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"Enter Command\n\u0026gt; Navigate 0 table_10\"\u003e\u003cpre\u003eEnter Command\n\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e Navigate 0 table_10\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ewould command agent 0 to execute the \"OracleNavSkill\" targeting Entity with name \u003ccode\u003etable_10\u003c/code\u003e. By default, a video of the full skill execution will be produced and displayed after skill completion along with skill output text printed to the console.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAll generated video will be saved to \u003ccode\u003epaths.results_dir\u003c/code\u003e, change this path by adding \u003ccode\u003epaths.results_dir=\u0026lt;relative_path_to_directory\u0026gt;\u003c/code\u003e to the CLI call.\u003c/li\u003e\n\u003cli\u003eVideo recording and playback can be disabled by setting \u003ccode\u003e+skill_runner_make_video=false\u003c/code\u003e in the CLI.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eNOTE: skill_runner currently supports only sequential skill execution for one agent at a time. For example, you cannot command both agents to simultaneously navigate. Primarily this restriction is motivated by the complexity of synchronizing skills with different execution lengths.\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAdditional non-skill commands include:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003edebug\u003c/code\u003e - trigger a pdb breakpoint for open-ended debugging and attach a \u003ccode\u003eDebugVisualizer\u003c/code\u003e to local variable \u003ccode\u003edbv\u003c/code\u003e for visual debugging.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eexit\u003c/code\u003e - quit the program.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ehelp\u003c/code\u003e - print the available skill text to the console.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eentities\u003c/code\u003e - print the list of available entities to the console.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emake_video\u003c/code\u003e - generate, display, and save a concatenation of all sequential skill execution videos.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsing with ConceptGraphs\u003c/h2\u003e\u003ca id=\"user-content-using-with-conceptgraphs\" class=\"anchor\" aria-label=\"Permalink: Using with ConceptGraphs\" href=\"#using-with-conceptgraphs\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee detailed doc \u003ca href=\"/facebookresearch/partnr-planner/blob/main/habitat_llm/concept_graphs/README.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFinetuning a planning model\u003c/h2\u003e\u003ca id=\"user-content-finetuning-a-planning-model\" class=\"anchor\" aria-label=\"Permalink: Finetuning a planning model\" href=\"#finetuning-a-planning-model\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee detailed doc \u003ca href=\"/facebookresearch/partnr-planner/blob/main/habitat_llm/finetuning/README.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExtending the environment\u003c/h2\u003e\u003ca id=\"user-content-extending-the-environment\" class=\"anchor\" aria-label=\"Permalink: Extending the environment\" href=\"#extending-the-environment\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor additional details about adding new agents, tools and custom actions see \u003ca href=\"/facebookresearch/partnr-planner/blob/main/docs/extending.md\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003epartnr-planner is MIT licensed. See the \u003ca href=\"/facebookresearch/partnr-planner/blob/main/LICENSE\"\u003eLICENSE\u003c/a\u003e for details.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-Agent Tasks","anchor":"partnr-a-benchmark-for-planning-and-reasoning-in-embodied-multi-agent-tasks","htmlText":"PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-Agent Tasks"},{"level":2,"text":"Overview","anchor":"overview","htmlText":"Overview"},{"level":2,"text":"Citing PARTNR","anchor":"citing-partnr","htmlText":"Citing PARTNR"},{"level":2,"text":"Code Organization","anchor":"code-organization","htmlText":"Code Organization"},{"level":2,"text":"Information Flow","anchor":"information-flow","htmlText":"Information Flow"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":2,"text":"Quickstart","anchor":"quickstart","htmlText":"Quickstart"},{"level":3,"text":"Decentralized Multi Agent React Summary","anchor":"decentralized-multi-agent-react-summary","htmlText":"Decentralized Multi Agent React Summary"},{"level":3,"text":"Centralized Multi Agent React Summary","anchor":"centralized-multi-agent-react-summary","htmlText":"Centralized Multi Agent React Summary"},{"level":3,"text":"Single Agent React Summary","anchor":"single-agent-react-summary","htmlText":"Single Agent React Summary"},{"level":3,"text":"Heuristic Planner","anchor":"heuristic-planner","htmlText":"Heuristic Planner"},{"level":3,"text":"Custom Instructions","anchor":"custom-instructions","htmlText":"Custom Instructions"},{"level":3,"text":"OpenAI Backend","anchor":"openai-backend","htmlText":"OpenAI Backend"},{"level":3,"text":"Learned Skills","anchor":"learned-skills","htmlText":"Learned Skills"},{"level":2,"text":"Calculating Results","anchor":"calculating-results","htmlText":"Calculating Results"},{"level":2,"text":"Testing episodes","anchor":"testing-episodes","htmlText":"Testing episodes"},{"level":4,"text":"SkillRunner: Running Oracle Skills in a Sandbox Environment","anchor":"skillrunner-running-oracle-skills-in-a-sandbox-environment","htmlText":"SkillRunner: Running Oracle Skills in a Sandbox Environment"},{"level":2,"text":"Using with ConceptGraphs","anchor":"using-with-conceptgraphs","htmlText":"Using with ConceptGraphs"},{"level":2,"text":"Finetuning a planning model","anchor":"finetuning-a-planning-model","htmlText":"Finetuning a planning model"},{"level":2,"text":"Extending the environment","anchor":"extending-the-environment","htmlText":"Extending the environment"},{"level":2,"text":"License","anchor":"license","htmlText":"License"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Ffacebookresearch%2Fpartnr-planner%2Ftree%2Fmain"}},{"displayName":"CODE_OF_CONDUCT.md","repoName":"partnr-planner","refName":"main","path":"CODE_OF_CONDUCT.md","preferredFileType":"code_of_conduct","tabName":"Code of 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1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-Agent Tasks</h1><a id="user-content-partnr-a-benchmark-for-planning-and-reasoning-in-embodied-multi-agent-tasks" class="anchor" aria-label="Permalink: PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-Agent Tasks" href="#partnr-a-benchmark-for-planning-and-reasoning-in-embodied-multi-agent-tasks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This is the official repository for the PARTNR benchmark. It includes instructions for downloading and running the PARTNR benchmark in the Habitat simulator and implements Large Language Model (LLM) based planners as baselines.</p> <p dir="auto"><a href="https://aihabitat.org/partnr/" rel="nofollow">Project Website</a> | <a href="https://arxiv.org/pdf/2411.00081" rel="nofollow">Paper</a></p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/facebookresearch/partnr-planner/blob/main/docs/cover_fig.png"><img src="/facebookresearch/partnr-planner/raw/main/docs/cover_fig.png" width="100%" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Overview</h2><a id="user-content-overview" class="anchor" aria-label="Permalink: Overview" href="#overview"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This codebase contains abstractions for enabling agents to follow free form natural language instructions in the Habitat simulator. The main abstractions are - <strong>Agent</strong>, <strong>Planner</strong>, <strong>Tool</strong>, and <strong>Skill</strong>. The agent has access to a set of tools which can allow it to perceive the state of the environment, or interact with the environment using low level skills. This repository also contains the codebase for generating PARTNR datasets using LLMs.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citing PARTNR</h2><a id="user-content-citing-partnr" class="anchor" aria-label="Permalink: Citing PARTNR" href="#citing-partnr"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you use our codebase or dataset in your research, please cite the <a href="https://arxiv.org/pdf/2411.00081" rel="nofollow">PARTNR</a> paper:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@misc{chang2024partnrbenchmarkplanningreasoning, title={PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks}, author={Matthew Chang and Gunjan Chhablani and Alexander Clegg and Mikael Dallaire Cote and Ruta Desai and Michal Hlavac and Vladimir Karashchuk and Jacob Krantz and Roozbeh Mottaghi and Priyam Parashar and Siddharth Patki and Ishita Prasad and Xavier Puig and Akshara Rai and Ram Ramrakhya and Daniel Tran and Joanne Truong and John M. Turner and Eric Undersander and Tsung-Yen Yang}, year={2024}, eprint={2411.00081}, archivePrefix={arXiv}, primaryClass={cs.RO}, url={https://arxiv.org/abs/2411.00081}, }"><pre class="notranslate"><code>@misc{chang2024partnrbenchmarkplanningreasoning, title={PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks}, author={Matthew Chang and Gunjan Chhablani and Alexander Clegg and Mikael Dallaire Cote and Ruta Desai and Michal Hlavac and Vladimir Karashchuk and Jacob Krantz and Roozbeh Mottaghi and Priyam Parashar and Siddharth Patki and Ishita Prasad and Xavier Puig and Akshara Rai and Ram Ramrakhya and Daniel Tran and Joanne Truong and John M. Turner and Eric Undersander and Tsung-Yen Yang}, year={2024}, eprint={2411.00081}, archivePrefix={arXiv}, primaryClass={cs.RO}, url={https://arxiv.org/abs/2411.00081}, } </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Code Organization</h2><a id="user-content-code-organization" class="anchor" aria-label="Permalink: Code Organization" href="#code-organization"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Below are the details of various important directories and classes.</p> <ul dir="auto"> <li><strong>habitat-llm</strong> <ul dir="auto"> <li><strong>Agent</strong> : Represents the robot or human. An agent can act in the environment.</li> <li><strong>Tools</strong> : Represents abstractions which enable the agent to perceive or act in the environment.</li> <li><strong>Planner</strong> : Represents centralized and decentralized planners.</li> <li><strong>LLM</strong> : Contains abstractions to represent Llama and GPT APIs.</li> <li><strong>WorldGraph</strong> : Contains a hierarchical world graph representing rooms, furniture, objects.</li> <li><strong>Perception</strong> : Contains a simulated perception pipeline which sends local detections to the world model.</li> <li><strong>Examples</strong>: Contains demos and evaluation programs to show or analyze the performance of planners.</li> <li><strong>EvaluationRunner</strong>: Represents an abstraction for running the planners.</li> <li><strong>Conf</strong> : Contains hydra config files for all classes.</li> <li><strong>Utils</strong> : Contains various utility methods required throughout the codebase.</li> <li><strong>Tests</strong> : Contains unit tests.</li> </ul> </li> <li><strong>scripts</strong> <ul dir="auto"> <li><strong>hitl_analysis</strong> : Contains scripts to analyze and replay human-in-the-loop traces.</li> <li><strong>prediviz</strong> : Contains visualization and annotation tools for PARTNR tasks.</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Information Flow</h2><a id="user-content-information-flow" class="anchor" aria-label="Permalink: Information Flow" href="#information-flow"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Below is a figure showing the flow of information through decentralized planners in PARTNR. Please note that at there is no explicit communication between the two decentralized planners. The <a href="/facebookresearch/partnr-planner/blob/main/habitat_llm/agent/env/environment_interface.py">EnvironmentInterface</a> reads observations for each agent and sends them to the <a href="/facebookresearch/partnr-planner/blob/main/habitat_llm/perception/perception_sim.py">Perception Module</a>. The processed observations are used to update the <a href="/facebookresearch/partnr-planner/blob/main/habitat_llm/world_model/world_graph.py">World Graph</a> and the <a href="/facebookresearch/partnr-planner/blob/main/habitat_llm/planner/planner.py">Planner</a> (based on an LLM or other system) uses the world graph and the task description select a <a href="/facebookresearch/partnr-planner/blob/main/habitat_llm/tools/tool.py">Tool</a> to interact with the environment.</p> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/facebookresearch/partnr-planner/blob/main/docs/planner_v3.png"><img src="/facebookresearch/partnr-planner/raw/main/docs/planner_v3.png" width="65%" style="max-width: 100%;"></a> </p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">For installation, refer to <a href="/facebookresearch/partnr-planner/blob/main/INSTALLATION.md">INSTALLATION.md</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Quickstart</h2><a id="user-content-quickstart" class="anchor" aria-label="Permalink: Quickstart" href="#quickstart"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Baselines for a variety of agent configuration are runnable with the provided configurations. Select any of the baselines from <a href="/facebookresearch/partnr-planner/blob/main/habitat_llm/conf/baselines">habitat_llm/conf/baselines</a>. You may need to specify one or two LLMs based on the baseline. Set the inference mode to <code>hf</code> and specify the huggingface model to use for inference. Model weights will be downloaded automatically if they are not found. Below are examples using Llama-3.1-8B running on the <code>val_mini</code> data split. Replace <code>val_mini</code> with one of <code>[train_2k, val, train, val_mini]</code> to run other splits.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Decentralized Multi Agent React Summary</h3><a id="user-content-decentralized-multi-agent-react-summary" class="anchor" aria-label="Permalink: Decentralized Multi Agent React Summary" href="#decentralized-multi-agent-react-summary"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python -m habitat_llm.examples.planner_demo --config-name baselines/decentralized_zero_shot_react_summary.yaml \ habitat.dataset.data_path="data/datasets/partnr_episodes/v0_0/val_mini.json.gz" \ evaluation.agents.agent_0.planner.plan_config.llm.inference_mode=hf \ evaluation.agents.agent_1.planner.plan_config.llm.inference_mode=hf \ evaluation.agents.agent_0.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct \ evaluation.agents.agent_1.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct"><pre>python -m habitat_llm.examples.planner_demo --config-name baselines/decentralized_zero_shot_react_summary.yaml \ habitat.dataset.data_path=<span class="pl-s"><span class="pl-pds">"</span>data/datasets/partnr_episodes/v0_0/val_mini.json.gz<span class="pl-pds">"</span></span> \ evaluation.agents.agent_0.planner.plan_config.llm.inference_mode=hf \ evaluation.agents.agent_1.planner.plan_config.llm.inference_mode=hf \ evaluation.agents.agent_0.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct \ evaluation.agents.agent_1.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Centralized Multi Agent React Summary</h3><a id="user-content-centralized-multi-agent-react-summary" class="anchor" aria-label="Permalink: Centralized Multi Agent React Summary" href="#centralized-multi-agent-react-summary"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python -m habitat_llm.examples.planner_demo --config-name baselines/centralized_zero_shot_react_summary.yaml \ habitat.dataset.data_path="data/datasets/partnr_episodes/v0_0/val_mini.json.gz" \ evaluation.planner.plan_config.llm.inference_mode=hf \ evaluation.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct"><pre>python -m habitat_llm.examples.planner_demo --config-name baselines/centralized_zero_shot_react_summary.yaml \ habitat.dataset.data_path=<span class="pl-s"><span class="pl-pds">"</span>data/datasets/partnr_episodes/v0_0/val_mini.json.gz<span class="pl-pds">"</span></span> \ evaluation.planner.plan_config.llm.inference_mode=hf \ evaluation.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Single Agent React Summary</h3><a id="user-content-single-agent-react-summary" class="anchor" aria-label="Permalink: Single Agent React Summary" href="#single-agent-react-summary"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python -m habitat_llm.examples.planner_demo --config-name baselines/single_agent_zero_shot_react_summary.yaml \ habitat.dataset.data_path="data/datasets/partnr_episodes/v0_0/val_mini.json.gz" \ evaluation.agents.agent_0.planner.plan_config.llm.inference_mode=hf \ evaluation.agents.agent_0.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct"><pre>python -m habitat_llm.examples.planner_demo --config-name baselines/single_agent_zero_shot_react_summary.yaml \ habitat.dataset.data_path=<span class="pl-s"><span class="pl-pds">"</span>data/datasets/partnr_episodes/v0_0/val_mini.json.gz<span class="pl-pds">"</span></span> \ evaluation.agents.agent_0.planner.plan_config.llm.inference_mode=hf \ evaluation.agents.agent_0.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Heuristic Planner</h3><a id="user-content-heuristic-planner" class="anchor" aria-label="Permalink: Heuristic Planner" href="#heuristic-planner"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python -m habitat_llm.examples.planner_demo --config-name baselines/heuristic_full_obs.yaml \ habitat.dataset.data_path="data/datasets/partnr_episodes/v0_0/val_mini.json.gz""><pre>python -m habitat_llm.examples.planner_demo --config-name baselines/heuristic_full_obs.yaml \ habitat.dataset.data_path=<span class="pl-s"><span class="pl-pds">"</span>data/datasets/partnr_episodes/v0_0/val_mini.json.gz<span class="pl-pds">"</span></span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Custom Instructions</h3><a id="user-content-custom-instructions" class="anchor" aria-label="Permalink: Custom Instructions" href="#custom-instructions"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">In addition to the PARTNR splits you can run custom instructions as well. Below is an example running a custom instruction using single agent react.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python -m habitat_llm.examples.planner_demo --config-name baselines/single_agent_zero_shot_react_summary.yaml \ instruction="<CUSTOM_INSTRUCTION>" \ mode='cli' \ evaluation.agents.agent_0.planner.plan_config.llm.inference_mode=hf \ evaluation.agents.agent_0.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct"><pre>python -m habitat_llm.examples.planner_demo --config-name baselines/single_agent_zero_shot_react_summary.yaml \ instruction=<span class="pl-s"><span class="pl-pds">"</span><CUSTOM_INSTRUCTION><span class="pl-pds">"</span></span> \ mode=<span class="pl-s"><span class="pl-pds">'</span>cli<span class="pl-pds">'</span></span> \ evaluation.agents.agent_0.planner.plan_config.llm.inference_mode=hf \ evaluation.agents.agent_0.planner.plan_config.llm.generation_params.engine=meta-llama/Meta-Llama-3-8B-Instruct</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">OpenAI Backend</h3><a id="user-content-openai-backend" class="anchor" aria-label="Permalink: OpenAI Backend" href="#openai-backend"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To run with openai chat as the LLM backed instead set the <code>plan_config.llm=openai_chat</code>. Make sure your API keys have been set appropriately.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python -m habitat_llm.examples.planner_demo --config-name baselines/single_agent_zero_shot_react_summary.yaml \ habitat.dataset.data_path="data/datasets/partnr_episodes/v0_0/val_mini.json.gz" \ llm@evaluation.agents.agent_0.planner.plan_config.llm=openai_chat"><pre>python -m habitat_llm.examples.planner_demo --config-name baselines/single_agent_zero_shot_react_summary.yaml \ habitat.dataset.data_path=<span class="pl-s"><span class="pl-pds">"</span>data/datasets/partnr_episodes/v0_0/val_mini.json.gz<span class="pl-pds">"</span></span> \ llm@evaluation.agents.agent_0.planner.plan_config.llm=openai_chat</pre></div> <p dir="auto">Note that constrained generation is not supported for the openai backend.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Learned Skills</h3><a id="user-content-learned-skills" class="anchor" aria-label="Permalink: Learned Skills" href="#learned-skills"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To run baselines with neural network skills for the robot agent (point nav, pick, and place) append the following argument overrides to the above commands. Please refer to the installation readme to download the skill checkpoints.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=" device=cpu \ agent@evaluation.agents.agent_0.config=nn_rearrange_agent_motortoolsonly \ habitat_conf/task=rearrange_easy_multi_agent_nn \"><pre> device=cpu \ agent@evaluation.agents.agent_0.config=nn_rearrange_agent_motortoolsonly \ habitat_conf/task=rearrange_easy_multi_agent_nn \</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Calculating Results</h2><a id="user-content-calculating-results" class="anchor" aria-label="Permalink: Calculating Results" href="#calculating-results"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can check the progress of the run and the results so far using <code>python scripts/read_results.py <output_dir>/<dataset_name></code>. The default <code>output_dir</code> is <code>outputs/habitat_llm/<timestamp>-<dataset_name></code>. This can be overridden using the <code>paths.results_dir</code> and <code>evaluation.output_dir</code> configuration elements. The output directory also contains the full text traces of the rollouts and other statistics.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Testing episodes</h2><a id="user-content-testing-episodes" class="anchor" aria-label="Permalink: Testing episodes" href="#testing-episodes"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To test whether a dataset is runnable and the success at step 0, run the following command. Set the <code>habitat.dataset.data_path</code> to the dataset you want to test.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="HYDRA_FULL_ERROR=1 python -m habitat_llm.examples.verify_episodes \ --config-name examples/planner_multi_agent_demo_config.yaml \ hydra.run.dir="." \ evaluation=centralized_evaluation_runner_multi_agent \ habitat.dataset.data_path="data/datasets/partnr_episodes/v0_0/val_mini.json.gz" \ mode='data' \ world_model.partial_obs='False' \ evaluation.type="centralized" \ num_proc=5"><pre>HYDRA_FULL_ERROR=1 python -m habitat_llm.examples.verify_episodes \ --config-name examples/planner_multi_agent_demo_config.yaml \ hydra.run.dir=<span class="pl-s"><span class="pl-pds">"</span>.<span class="pl-pds">"</span></span> \ evaluation=centralized_evaluation_runner_multi_agent \ habitat.dataset.data_path=<span class="pl-s"><span class="pl-pds">"</span>data/datasets/partnr_episodes/v0_0/val_mini.json.gz<span class="pl-pds">"</span></span> \ mode=<span class="pl-s"><span class="pl-pds">'</span>data<span class="pl-pds">'</span></span> \ world_model.partial_obs=<span class="pl-s"><span class="pl-pds">'</span>False<span class="pl-pds">'</span></span> \ evaluation.type=<span class="pl-s"><span class="pl-pds">"</span>centralized<span class="pl-pds">"</span></span> \ num_proc=5</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">SkillRunner: Running Oracle Skills in a Sandbox Environment</h4><a id="user-content-skillrunner-running-oracle-skills-in-a-sandbox-environment" class="anchor" aria-label="Permalink: SkillRunner: Running Oracle Skills in a Sandbox Environment" href="#skillrunner-running-oracle-skills-in-a-sandbox-environment"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The habitat_llm/examples/skill_runner.py application provides a headless command line interface (CLI) for running custom sequences of oracle skills within a sandbox environment for a given episode.</p> <p dir="auto">For example:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="HYDRA_FULL_ERROR=1 python -m habitat_llm.examples.skill_runner hydra.run.dir="." +skill_runner_show_topdown=True habitat.dataset.data_path=data/datasets/partnr_episodes/v0_0/val_mini.json.gz +skill_runner_episode_id="334""><pre>HYDRA_FULL_ERROR=1 python -m habitat_llm.examples.skill_runner hydra.run.dir=<span class="pl-s"><span class="pl-pds">"</span>.<span class="pl-pds">"</span></span> +skill_runner_show_topdown=True habitat.dataset.data_path=data/datasets/partnr_episodes/v0_0/val_mini.json.gz +skill_runner_episode_id=<span class="pl-s"><span class="pl-pds">"</span>334<span class="pl-pds">"</span></span></pre></div> <p dir="auto">In the above example, episode <code>334</code> from the <code>val_mini.json.gz</code> dataset will be instantiated, the list of all active Entities (Rooms, Objects, Furniture, and Receptacles) will be printed to the console.</p> <ul dir="auto"> <li> <p dir="auto">By providing CLI option <code>+skill_runner_show_topdown=True</code>, the program will render and display a top-down view of the scene upon initialization.</p> </li> <li> <p dir="auto">CLI option <code>+skill_runner_episode_index=</code> can be provided instead of <code>+skill_runner_episode_id=</code>, in which case the integer index will be used to query the episode from the dataset.</p> </li> <li> <p dir="auto">Note that all environment config values can be overridden from the CLI and defaults are set in <code>habitat_llm/conf/examples/skill_runner_default_config.yaml</code>.</p> </li> </ul> <p dir="auto">Once the Episode is initialized, the user will be presented with a list of available skills (e.g. Navigate, Pick, Place, Open, Close) and prompted to enter a command.</p> <p dir="auto">Commands can take the form of a skill call, for example:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="Enter Command > Navigate 0 table_10"><pre>Enter Command <span class="pl-k">></span> Navigate 0 table_10</pre></div> <p dir="auto">would command agent 0 to execute the "OracleNavSkill" targeting Entity with name <code>table_10</code>. By default, a video of the full skill execution will be produced and displayed after skill completion along with skill output text printed to the console.</p> <ul dir="auto"> <li>All generated video will be saved to <code>paths.results_dir</code>, change this path by adding <code>paths.results_dir=<relative_path_to_directory></code> to the CLI call.</li> <li>Video recording and playback can be disabled by setting <code>+skill_runner_make_video=false</code> in the CLI.</li> </ul> <p dir="auto"><em>NOTE: skill_runner currently supports only sequential skill execution for one agent at a time. For example, you cannot command both agents to simultaneously navigate. Primarily this restriction is motivated by the complexity of synchronizing skills with different execution lengths.</em></p> <p dir="auto">Additional non-skill commands include:</p> <ul dir="auto"> <li><code>debug</code> - trigger a pdb breakpoint for open-ended debugging and attach a <code>DebugVisualizer</code> to local variable <code>dbv</code> for visual debugging.</li> <li><code>exit</code> - quit the program.</li> <li><code>help</code> - print the available skill text to the console.</li> <li><code>entities</code> - print the list of available entities to the console.</li> <li><code>make_video</code> - generate, display, and save a concatenation of all sequential skill execution videos.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Using with ConceptGraphs</h2><a id="user-content-using-with-conceptgraphs" class="anchor" aria-label="Permalink: Using with ConceptGraphs" href="#using-with-conceptgraphs"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">See detailed doc <a href="/facebookresearch/partnr-planner/blob/main/habitat_llm/concept_graphs/README.md">here</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Finetuning a planning model</h2><a id="user-content-finetuning-a-planning-model" class="anchor" aria-label="Permalink: Finetuning a planning model" href="#finetuning-a-planning-model"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">See detailed doc <a href="/facebookresearch/partnr-planner/blob/main/habitat_llm/finetuning/README.md">here</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Extending the environment</h2><a id="user-content-extending-the-environment" class="anchor" aria-label="Permalink: Extending the environment" href="#extending-the-environment"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">For additional details about adding new agents, tools and custom actions see <a href="/facebookresearch/partnr-planner/blob/main/docs/extending.md">here</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">License</h2><a id="user-content-license" class="anchor" aria-label="Permalink: License" href="#license"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">partnr-planner is MIT licensed. See the <a href="/facebookresearch/partnr-planner/blob/main/LICENSE">LICENSE</a> for details.</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="iUGNaXbmaeMdmNxjtr8xOODr1lyARsG0bRUADFa4prlX5p/GINqStBZgI39TdfQTO6mMBD3BeXkASvA24b432g==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> A repository accompanying the PARTNR benchmark for using Large Planning Models (LPMs) to solve Human-Robot Collaboration or Robot Instruction Following tasks in the Habitat simulator. </p> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{"category":"Repository 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properties</span> </a> </div> <h3 class="sr-only">Stars</h3> <div class="mt-2"> <a href="/facebookresearch/partnr-planner/stargazers" data-view-component="true" class="Link Link--muted"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>68</strong> stars </a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/facebookresearch/partnr-planner/watchers" data-view-component="true" class="Link Link--muted"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>8</strong> watching </a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/facebookresearch/partnr-planner/forks" data-view-component="true" class="Link Link--muted"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>3</strong> forks </a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Ffacebookresearch%2Fpartnr-planner&report=facebookresearch+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" 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