BodyGen: Advancing Towards Efficient Embodiment Co-Design
Keywords: Reinforcement Learning
TL;DR: This paper introduces BodyGen: a novel framework for efficient embodiment co-design.
Abstract: Embodiment co-design aims to optimize a robot's morphology and control policy simultaneously.
While prior work has demonstrated its potential for generating environment-adaptive robots, this field still faces persistent challenges in optimization efficiency due to the (i) combinatorial nature of morphological search spaces and (ii) intricate dependencies between morphology and control.
We prove that the ineffective morphology representation and unbalanced reward signals between the design and control stages are key obstacles to efficiency.
To advance towards efficient embodiment co-design, we propose **BodyGen**, which utilizes (1) topology-aware self-attention for both design and control, enabling efficient morphology representation with lightweight model sizes; (2) a temporal credit assignment mechanism that ensures balanced reward signals for optimization. With our findings, BodyGen achieves an average **60.03%** performance improvement against state-of-the-art baselines. We provide codes and more results on the website: https://genesisorigin.github.io.
Primary Area: reinforcement learning
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Submission Number: 5415
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