提出BodyGen，提升机器人形态与控制协同设计的效率
BodyGen: Advancing Towards Efficient Embodiment Co-Design
- 用拓扑感知自注意力实现轻量级形态表示
- 性能较现有方法平均提升60.03%
- 适合机器人设计与强化学习研究者参考
体感协同设计旨在同时优化机器人的形态与控制策略。尽管先前工作已展示其生成环境适应型机器人的潜力，但该领域仍面临优化效率低下的挑战，主要源于形态搜索空间的组合复杂性及形态与控制间的复杂依赖关系。我们证明，无效的形态表示和设计与控制阶段奖励信号不平衡是效率低下的关键障碍。为此，我们提出BodyGen，采用（1）拓扑感知自注意力机制，分别用于设计与控制，实现高效形态表示且模型轻量化；（2）时间信用分配机制，确保优化过程中奖励信号均衡。实验表明，BodyGen相较当前最优基线平均性能提升60.03%。代码与更多结果详见：https://genesisorigin.github.io。
原文摘要 · Abstract (English)
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 to 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, Body achieves an average 60.03% performance improvement against state-of-the-art baselines. We provide codes and more results on the website: https://genesisorigin.github.io.
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