Yuan, K;
Li, Z;
(2022)
Multi-expert synthesis for versatile locomotion and manipulation skills.
Frontiers in Robotics and AI
, 9
, Article 970890. 10.3389/frobt.2022.970890.
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Abstract
This work focuses on generating multiple coordinated motor skills for intelligent systems and studies a Multi-Expert Synthesis (MES) approach to achieve versatile robotic skills for locomotion and manipulation. MES embeds and uses expert skills to solve new composite tasks, and is able to synthesise and coordinate different and multiple skills smoothly. We proposed essential and effective design guidelines for training successful MES policies in simulation, which were deployed on both floating- and fixed-base robots. We formulated new algorithms to systematically determine task-relevant state variables for each individual experts which improved robustness and learning efficiency, and an explicit enforcement objective to diversify skills among different experts. The capabilities of MES policies were validated in both simulation and real experiments for locomotion and bi-manual manipulation. We demonstrated that the MES policies achieved robust locomotion on the quadruped ANYmal by fusing the gait recovery and trotting skills. For object manipulation, the MES policies learned to first reconfigure an object in an ungraspable pose and then grasp it through cooperative dual-arm manipulation.
| Type: | Article |
|---|---|
| Title: | Multi-expert synthesis for versatile locomotion and manipulation skills |
| Location: | Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/frobt.2022.970890 |
| Publisher version: | https://doi.org/10.3389/frobt.2022.970890 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Legged robots, manipulation, multi-expert learning, reinforcement learning, robot learning, robotics, versatile locomotion |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10158171 |
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