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cs.RO2026
TacSushi: Tactile-Grounded World-Action Modeling for Dexterous Sushi Manipulation
Haodi Hu, Kaen Kogashi, Toshiaki Koike-Akino
Dexterous food manipulation requires control under deformation, occlusion, and uncertain contact. We present TacSushi, a tactile-grounded, Cosmos3-based world-action policy that le…
cs.RO2026
ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies
Haodi Hu, Chung-Ta Huang, Jing Liu +4
Vision-language-action (VLA) policies provide strong priors for language-conditioned manipulation, but remain brittle in off-nominal states requiring targeted recovery. We propose…
cs.RO2026
Embedding Morphology into Transformers for Cross-Robot Policy Learning
Kei Suzuki, Jing Liu, Ye Wang +4
Cross-robot policy learning -- training a single policy to perform well across multiple embodiments -- remains a central challenge in robot learning. Transformer-based policies, su…