BodyGen: Advancing Towards Efficient Embodiment Co-Design

Haofei Lu1, Zhe Wu1, Junliang Xing1, Jianshu Li2, Ruoyu Li2, Zhe Li2, Yuanchun Shi1,
1Tsinghua University, 2Ant Group
Paper Code

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 against 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.

Random Embodied Agents generated by our embodiment co-design approach, BodyGen .

Methods

BodyGen is an RL-based framework for embodiment co-design, which optimizes morphology and control simultaneously. It achieves centralized, zero-decay message processing based on limb-level self-attention, message localization via Topology Position Encoding, and enhanced temporal credit assignment for balanced reward signals.

For embodiment co-design, besides differentiating message source within the body, we require an evolution-aware morphology representation. We propose Topology Position Encoding (TopoPE), to better adapt to the evolving process. It not only achieves message localization but also faciliates knowledge sharing within the dynamically growing body.

Visualization

We randomly pick embodied agents generated by BodyGen for visualization.

Crawler

TerrainCrosser

Cheetah

Swimmer

Glider-Regular

Glider-Medium

Glider-Hard

Walker-Regular

Walker-Medium

Walker-Hard

Experiments

Experiments show that BodyGen achieves an average 60.03% performance improvement over the strongest baselines. Notably, each model generated by BodyGen is only with 1.4 M parameters, but has already exhibited exciting capabilities in morphology evolution and motion control, demonstrating promising potential for scaling-up towards more complex embodied agents and embodiment systems in the expected future.