Multi-critic Learning for Whole-body End-effector Twist Tracking
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914012578447360 |
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| author | Vijayan, Aravind Elanjimattathil Cramariuc, Andrei Risiglione, Mattia Gehring, Christian Hutter, Marco |
| author_facet | Vijayan, Aravind Elanjimattathil Cramariuc, Andrei Risiglione, Mattia Gehring, Christian Hutter, Marco |
| contents | Learning whole-body control for locomotion and arm motions in a single policy has challenges, as the two tasks have conflicting goals. For instance, efficient locomotion typically favors a horizontal base orientation, while end-effector tracking may benefit from base tilting to extend reachability. Additionally, current Reinforcement Learning (RL) approaches using a pose-based task specification lack the ability to directly control the end-effector velocity, making smoothly executing trajectories very challenging. To address these limitations, we propose an RL-based framework that allows for dynamic, velocity-aware whole-body end-effector control. Our method introduces a multi-critic actor architecture that decouples the reward signals for locomotion and manipulation, simplifying reward tuning and allowing the policy to resolve task conflicts more effectively. Furthermore, we design a twist-based end-effector task formulation that can track both discrete poses and motion trajectories. We validate our approach through a set of simulation and hardware experiments using a quadruped robot equipped with a robotic arm. The resulting controller can simultaneously walk and move its end-effector and shows emergent whole-body behaviors, where the base assists the arm in extending the workspace, despite a lack of explicit formulations. Videos and supplementary material can be found at multi-critic-locomanipulation.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08656 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-critic Learning for Whole-body End-effector Twist Tracking Vijayan, Aravind Elanjimattathil Cramariuc, Andrei Risiglione, Mattia Gehring, Christian Hutter, Marco Robotics Learning whole-body control for locomotion and arm motions in a single policy has challenges, as the two tasks have conflicting goals. For instance, efficient locomotion typically favors a horizontal base orientation, while end-effector tracking may benefit from base tilting to extend reachability. Additionally, current Reinforcement Learning (RL) approaches using a pose-based task specification lack the ability to directly control the end-effector velocity, making smoothly executing trajectories very challenging. To address these limitations, we propose an RL-based framework that allows for dynamic, velocity-aware whole-body end-effector control. Our method introduces a multi-critic actor architecture that decouples the reward signals for locomotion and manipulation, simplifying reward tuning and allowing the policy to resolve task conflicts more effectively. Furthermore, we design a twist-based end-effector task formulation that can track both discrete poses and motion trajectories. We validate our approach through a set of simulation and hardware experiments using a quadruped robot equipped with a robotic arm. The resulting controller can simultaneously walk and move its end-effector and shows emergent whole-body behaviors, where the base assists the arm in extending the workspace, despite a lack of explicit formulations. Videos and supplementary material can be found at multi-critic-locomanipulation.github.io. |
| title | Multi-critic Learning for Whole-body End-effector Twist Tracking |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.08656 |