Reinforcement learning for freeform robot design

Muhan Li; David Matthews; Sam Kriegman

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arXiv · 2310.05670

Reinforcement learning for freeform robot design

Muhan Li·David Matthews·Sam Kriegman

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Abstract

Inspired by the necessity of morphological adaptation in animals, a growing body of work has attempted to expand robot training to encompass physical aspects of a robot's design. However, reinforcement learning methods capable of optimizing the 3D morphology of a robot have been restricted to reorienting or resizing the limbs of a predetermined and static topological genus. Here we show policy gradients for designing freeform robots with arbitrary external and internal structure. This is achieved through actions that deposit or remove bundles of atomic building blocks to form higher-level nonparametric macrostructures such as appendages, organs and cavities. Although results are provided for open loop control only, we discuss how this method could be adapted for closed loop control and sim2real transfer to physical machines in future.

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Muhan Li, David Matthews, Sam Kriegman. 2023-10-09. Reinforcement learning for freeform robot design. https://arxiv.org/abs/2310.05670

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