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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.05811">arXiv:2409.05811</a> <span> [<a href="https://arxiv.org/pdf/2409.05811">pdf</a>, <a href="https://arxiv.org/format/2409.05811">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Learning control of underactuated double pendulum with Model-Based Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Turcato%2C+N">Niccol貌 Turcato</a>, <a href="/search/cs?searchtype=author&query=Libera%2C+A+D">Alberto Dalla Libera</a>, <a href="/search/cs?searchtype=author&query=Giacomuzzo%2C+G">Giulio Giacomuzzo</a>, <a href="/search/cs?searchtype=author&query=Carli%2C+R">Ruggero Carli</a>, <a href="/search/cs?searchtype=author&query=Romeres%2C+D">Diego Romeres</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.05811v1-abstract-short" style="display: inline;"> This report describes our proposed solution for the second AI Olympics competition held at IROS 2024. Our solution is based on a recent Model-Based Reinforcement Learning algorithm named MC-PILCO. Besides briefly reviewing the algorithm, we discuss the most critical aspects of the MC-PILCO implementation in the tasks at hand. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.05811v1-abstract-full" style="display: none;"> This report describes our proposed solution for the second AI Olympics competition held at IROS 2024. Our solution is based on a recent Model-Based Reinforcement Learning algorithm named MC-PILCO. Besides briefly reviewing the algorithm, we discuss the most critical aspects of the MC-PILCO implementation in the tasks at hand. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05811v1-abstract-full').style.display = 'none'; document.getElementById('2409.05811v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.16125">arXiv:2408.16125</a> <span> [<a href="https://arxiv.org/pdf/2408.16125">pdf</a>, <a href="https://arxiv.org/format/2408.16125">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> DECAF: a Discrete-Event based Collaborative Human-Robot Framework for Furniture Assembly </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Giacomuzzo%2C+G">Giulio Giacomuzzo</a>, <a href="/search/cs?searchtype=author&query=Terreran%2C+M">Matteo Terreran</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+S">Siddarth Jain</a>, <a href="/search/cs?searchtype=author&query=Romeres%2C+D">Diego Romeres</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.16125v1-abstract-short" style="display: inline;"> This paper proposes a task planning framework for collaborative Human-Robot scenarios, specifically focused on assembling complex systems such as furniture. The human is characterized as an uncontrollable agent, implying for example that the agent is not bound by a pre-established sequence of actions and instead acts according to its own preferences. Meanwhile, the task planner computes reactively… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.16125v1-abstract-full').style.display = 'inline'; document.getElementById('2408.16125v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.16125v1-abstract-full" style="display: none;"> This paper proposes a task planning framework for collaborative Human-Robot scenarios, specifically focused on assembling complex systems such as furniture. The human is characterized as an uncontrollable agent, implying for example that the agent is not bound by a pre-established sequence of actions and instead acts according to its own preferences. Meanwhile, the task planner computes reactively the optimal actions for the collaborative robot to efficiently complete the entire assembly task in the least time possible. We formalize the problem as a Discrete Event Markov Decision Problem (DE-MDP), a comprehensive framework that incorporates a variety of asynchronous behaviors, human change of mind and failure recovery as stochastic events. Although the problem could theoretically be addressed by constructing a graph of all possible actions, such an approach would be constrained by computational limitations. The proposed formulation offers an alternative solution utilizing Reinforcement Learning to derive an optimal policy for the robot. Experiments where conducted both in simulation and on a real system with human subjects assembling a chair in collaboration with a 7-DoF manipulator. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.16125v1-abstract-full').style.display = 'none'; document.getElementById('2408.16125v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 6 figures, extended version of accepted paper at IRO24</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.14338">arXiv:2406.14338</a> <span> [<a href="https://arxiv.org/pdf/2406.14338">pdf</a>, <a href="https://arxiv.org/format/2406.14338">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Adaptive Robust Controller for handling Unknown Uncertainty of Robotic Manipulators </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abdelwahab%2C+M">Mohamed Abdelwahab</a>, <a href="/search/cs?searchtype=author&query=Giacomuzzo%2C+G">Giulio Giacomuzzo</a>, <a href="/search/cs?searchtype=author&query=Libera%2C+A+D">Alberto Dalla Libera</a>, <a href="/search/cs?searchtype=author&query=Carli%2C+R">Ruggero Carli</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.14338v1-abstract-short" style="display: inline;"> The ability to achieve precise and smooth trajectory tracking is crucial for ensuring the successful execution of various tasks involving robotic manipulators. State-of-the-art techniques require accurate mathematical models of the robot dynamics, and robustness to model uncertainties is achieved by relying on precise bounds on the model mismatch. In this paper, we propose a novel adaptive robust… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14338v1-abstract-full').style.display = 'inline'; document.getElementById('2406.14338v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.14338v1-abstract-full" style="display: none;"> The ability to achieve precise and smooth trajectory tracking is crucial for ensuring the successful execution of various tasks involving robotic manipulators. State-of-the-art techniques require accurate mathematical models of the robot dynamics, and robustness to model uncertainties is achieved by relying on precise bounds on the model mismatch. In this paper, we propose a novel adaptive robust feedback linearization scheme able to compensate for model uncertainties without any a-priori knowledge on them, and we provide a theoretical proof of convergence under mild assumptions. We evaluate the method on a simulated RR robot. First, we consider a nominal model with known model mismatch, which allows us to compare our strategy with state-of-the-art uncertainty-aware methods. Second, we implement the proposed control law in combination with a learned model, for which uncertainty bounds are not available. Results show that our method leads to performance comparable to uncertainty-aware methods while requiring less prior knowledge. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14338v1-abstract-full').style.display = 'none'; document.getElementById('2406.14338v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.05093">arXiv:2307.05093</a> <span> [<a href="https://arxiv.org/pdf/2307.05093">pdf</a>, <a href="https://arxiv.org/format/2307.05093">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Forward Dynamics Estimation from Data-Driven Inverse Dynamics Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Libera%2C+A+D">Alberto Dalla Libera</a>, <a href="/search/cs?searchtype=author&query=Giacomuzzo%2C+G">Giulio Giacomuzzo</a>, <a href="/search/cs?searchtype=author&query=Carli%2C+R">Ruggero Carli</a>, <a href="/search/cs?searchtype=author&query=Nikovski%2C+D">Daniel Nikovski</a>, <a href="/search/cs?searchtype=author&query=Romeres%2C+D">Diego Romeres</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.05093v1-abstract-short" style="display: inline;"> In this paper, we propose to estimate the forward dynamics equations of mechanical systems by learning a model of the inverse dynamics and estimating individual dynamics components from it. We revisit the classical formulation of rigid body dynamics in order to extrapolate the physical dynamical components, such as inertial and gravitational components, from an inverse dynamics model. After estima… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.05093v1-abstract-full').style.display = 'inline'; document.getElementById('2307.05093v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.05093v1-abstract-full" style="display: none;"> In this paper, we propose to estimate the forward dynamics equations of mechanical systems by learning a model of the inverse dynamics and estimating individual dynamics components from it. We revisit the classical formulation of rigid body dynamics in order to extrapolate the physical dynamical components, such as inertial and gravitational components, from an inverse dynamics model. After estimating the dynamical components, the forward dynamics can be computed in closed form as a function of the learned inverse dynamics. We tested the proposed method with several machine learning models based on Gaussian Process Regression and compared them with the standard approach of learning the forward dynamics directly. Results on two simulated robotic manipulators, a PANDA Franka Emika and a UR10, show the effectiveness of the proposed method in learning the forward dynamics, both in terms of accuracy as well as in opening the possibility of using more structured~models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.05093v1-abstract-full').style.display = 'none'; document.getElementById('2307.05093v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" 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