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cs.RO2026
Training Controllable World Models via Action Influence Maximization
Samuel M. Bateman, Tenny Yin, Chongyi Zheng +5
Action-conditioned video generative models trained on successful, goal-directed demonstrations tend to ignore the commanded action, rendering a plausible expert-like future regardl…
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
PlayWorld: Learning Robot World Models from Autonomous Play
Tenny Yin, Zhiting Mei, Zhonghe Zheng +8
Action-conditioned video models offer a promising path to building general-purpose robot simulators that can improve directly from data. Yet, despite training on large-scale robot…