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arXiv · 2603.00182

Embedding Morphology into Transformers for Cross-Robot Policy Learning

Kei Suzuki·Jing Liu·Ye Wang·Chiori Hori·Matthew Brand·Diego Romeres·Toshiaki Koike-Akino
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Abstract

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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BibTeXRIS

Kei Suzuki, Jing Liu, Ye Wang, Chiori Hori, Matthew Brand, Diego Romeres, Toshiaki Koike-Akino. 2026-02-26. Embedding Morphology into Transformers for Cross-Robot Policy Learning. https://arxiv.org/abs/2603.00182

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