The content discusses the use of reinforcement learning methods to optimize the design of freeform robots with unique external and internal structures. By depositing or removing bundles of atomic building blocks, higher-level macrostructures like appendages, organs, and cavities can be formed. The study highlights the limitations of previous methods restricted to resizing limbs or altering predefined topologies. The proposed approach uses thousands of voxels as building blocks to optimize limb number, placement, and 3D shape simultaneously. It also addresses challenges in altering robot topology and emphasizes the importance of voids for various functionalities. The authors compare their method to previous studies using differentiable simulation for robot design but highlight the novelty in their approach by allowing changes in robot topology during optimization. They discuss how their policy-gradient method can lead to de novo optimization of nonparametric body plans while acknowledging limitations that may be addressed in future research. The results demonstrate successful training of policies for designing large coherent bodies and self-moving robots through optimizing body volume and locomotion efficiency.
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by Muhan Li,Dav... kl. arxiv.org 03-05-2024
https://arxiv.org/pdf/2310.05670.pdfDybere Forespørgsler