Meta Reinforcement Learning for Optimal Design of Legged Robots
- URL: http://arxiv.org/abs/2210.02750v1
- Date: Thu, 6 Oct 2022 08:37:52 GMT
- Title: Meta Reinforcement Learning for Optimal Design of Legged Robots
- Authors: \'Alvaro Belmonte-Baeza, Joonho Lee, Giorgio Valsecchi, Marco Hutter
- Abstract summary: We present a design optimization framework using model-free meta reinforcement learning.
We show that our approach allows higher performance while not being constrained by predefined motions or gait patterns.
- Score: 9.054187238463212
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The process of robot design is a complex task and the majority of design
decisions are still based on human intuition or tedious manual tuning. A more
informed way of facing this task is computational design methods where design
parameters are concurrently optimized with corresponding controllers. Existing
approaches, however, are strongly influenced by predefined control rules or
motion templates and cannot provide end-to-end solutions. In this paper, we
present a design optimization framework using model-free meta reinforcement
learning, and its application to the optimizing kinematics and actuator
parameters of quadrupedal robots. We use meta reinforcement learning to train a
locomotion policy that can quickly adapt to different designs. This policy is
used to evaluate each design instance during the design optimization. We
demonstrate that the policy can control robots of different designs to track
random velocity commands over various rough terrains. With controlled
experiments, we show that the meta policy achieves close-to-optimal performance
for each design instance after adaptation. Lastly, we compare our results
against a model-based baseline and show that our approach allows higher
performance while not being constrained by predefined motions or gait patterns.
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