让Transformer模型理解机器人身体结构，提升跨机型泛化能力。
Embedding Morphology into Transformers for Cross-Robot Policy Learning
- 用关节动作令牌和分块时间压缩，显式建模身体结构
- 通过拓扑注意力偏置，引导信息沿机械连接路径传播
- 结合关节属性描述，捕捉超越连接关系的语义信息
跨机器人策略学习——训练一个策略在多种机器人形态上均表现良好——仍是机器人学习中的核心挑战。基于Transformer的策略（如视觉-语言-动作模型）通常不依赖具体形态，需仅从观测中推断运动学结构，这会降低不同形态间的鲁棒性，甚至影响单个形态内的性能。本文提出一种形态感知的Transformer策略，通过三种机制注入形态信息：(1) 运动学令牌，将动作按关节分解，并通过每关节的时间分块压缩序列长度；(2) 拓扑感知注意力偏置，在自注意力中引入运动学拓扑作为归纳偏置，鼓励信息沿机械连接边传递；(3) 关节属性条件，用每个关节的描述符增强拓扑信息，捕捉连接关系之外的语义。在多种机器人形态上，该结构化集成方法持续优于基础的pi0.5 VLA模型，表明其在单个形态内及跨形态间均具有更强鲁棒性。
原文摘要 · Abstract (English)
Cross-robot policy learning -- training a single policy to perform well across multiple embodiments -- remains a central challenge in robot learning. Transformer-based policies, such as vision-language-action (VLA) models, are typically embodiment-agnostic and must infer kinematic structure purely from observations, which can reduce robustness across embodiments and even limit performance within a single embodiment. We propose an embodiment-aware transformer policy that injects morphology via three mechanisms: (1) kinematic tokens that factorize actions across joints and compress time through per-joint temporal chunking; (2) a topology-aware attention bias that encodes kinematic topology as an inductive bias in self-attention, encouraging message passing along kinematic edges; and (3) joint-attribute conditioning that augments topology with per-joint descriptors to capture semantics beyond connectivity. Across a range of embodiments, this structured integration consistently improves performance over a vanilla pi0.5 VLA baseline, indicating improved robustness both within an embodiment and across embodiments.
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