AINeutralarXiv – CS AI · Mar 34/104
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Embedding Morphology into Transformers for Cross-Robot Policy Learning
Researchers developed an embodiment-aware transformer policy that improves cross-robot policy learning by injecting morphological information through kinematic tokens, topology-aware attention, and joint-attribute conditioning. This approach consistently outperforms baseline vision-language-action models across multiple robot embodiments.