Shape Your Body: Value Gradients for Multi-Embodiment Robot Design
We propose to turn generalist multi-embodiment value functions into reusable models for robot design. Instead of running a new reinforcement learning co-design loop for each robot, we first train an embodiment-aware policy and value function across many robot designs. After training, the frozen value function is used as a differentiable surrogate to optimize candidate embodiments through value gradients. We evaluate our approach across different robot design settings, from perturbed single robots to held-out robots across morphology classes, with single models trained on up to 50 robots and design spaces of over 1100 continuous embodiment parameters. Beyond optimizing complete embodiments...
| Authors | Nico Bohlinger, Jan Peters |
|---|---|
| Primary category | cs.RO |
| Published | 30 May 2026 |
| Last updated at source | 13 October 2026 |
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