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cs.RO2026
EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning
Lihan Zha, Shresth Grover, Tenny Yin +7
Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a poor supervisory target for c…
cs.RO2026
SUAVE: Unified Video-Action Models via Masked Diffusion
Rhythm Syed, Jean Mercat, Sedrick Keh +5
Vision-language-action models (VLAs) inherit strong semantic grounding from pretrained vision-language backbones but are typically optimized for predicting actions rather than futu…
cs.RO2026
Interactive World Simulator for Robot Policy Training and Evaluation
Yixuan Wang, Rhythm Syed, Fangyu Wu +7
Action-conditioned video prediction models (often referred to as world models) have shown strong potential for robotics applications, but existing approaches are often slow and str…