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Multi-expert synthesis for versatile locomotion and manipulation skills
1School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom.
Frontiers in Robotics and AI
|October 17, 2022
Summary
This study introduces Multi-Expert Synthesis (MES) to generate coordinated robotic skills for locomotion and manipulation. MES enables robots to combine and control diverse skills for complex tasks, enhancing versatility.
Area of Science:
- Robotics
- Artificial Intelligence
- Machine Learning
Background:
- Intelligent systems require versatile motor skills for complex tasks.
- Current approaches often lack smooth coordination between multiple skills.
- Robotic locomotion and manipulation demand adaptable and robust skill sets.
Purpose of the Study:
- To develop and validate a Multi-Expert Synthesis (MES) approach for generating coordinated motor skills.
- To enable intelligent systems to perform versatile locomotion and manipulation tasks.
- To improve the robustness and learning efficiency of robotic skill policies.
Main Methods:
- Developed a Multi-Expert Synthesis (MES) framework integrating expert skills for composite tasks.
- Proposed design guidelines for training MES policies in simulation.
- Formulated algorithms for determining task-relevant state variables and enforcing skill diversification.
- Deployed and validated policies on floating- and fixed-base robots, including quadrupedal locomotion and dual-arm manipulation.
Main Results:
- MES policies demonstrated robust locomotion on the ANYmal quadruped by fusing gait recovery and trotting skills.
- Achieved cooperative dual-arm manipulation, including reconfiguring objects for grasping.
- Validated MES capabilities in both simulation and real-world experiments.
- Showcased improved robustness and learning efficiency through systematic state variable determination and skill diversification.
Conclusions:
- The Multi-Expert Synthesis (MES) approach effectively generates versatile and coordinated motor skills for intelligent systems.
- MES enables robots to seamlessly combine and control diverse skills for complex locomotion and manipulation tasks.
- The proposed methods enhance learning efficiency and policy robustness in real-world robotic applications.
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