Learning Purposeful Co-designs of Bio-inspired Quadrupedal Robots
Overview
This paper introduces Dogger, a bio-inspired quadruped featuring modular three-segment legs, four-bar linkages, and adjustable passive knee compliance. The authors propose a two-stage framework combining reinforcement learning with Bayesian optimization to train morphology-conditioned control policies across designs, which are then fixed during task-specific design selection. Deployed zero-shot, the resulting designs reduce cost of transport by 18.6% for inspection and 35.7% for payload-carrying tasks compared to baselines. The work demonstrates that expanding physical design space alongside learned control is essential for realizing task-driven morphology-control co-design in legged robotics.
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- arXiv Robotics — research abstractsSignalLearning Purposeful Co-designs of Bio-inspired Quadrupedal Robots
This paper introduces Dogger, a bio-inspired quadruped featuring modular three-segment legs, four-bar linkages, and adjustable passive knee compliance. The authors propose a two-stage framework combining reinforcement learning with Bayesian optimization to train morphology-conditioned control policies across designs, which are then fixed during task-specific design selection. Deployed zero-shot, the resulting designs reduce cost of transport by 18.6% for inspection and 35.7% for payload-carrying tasks compared to baselines. The work demonstrates that expanding physical design space alongside learned control is essential for realizing task-driven morphology-control co-design in legged robotics.
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