1 citations · 1 across the 3 of their papers we have counts for
5 papers
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…
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…
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…
Evaluating Global Geo-alignment for Precision Learned Autonomous Vehicle Localization using Aerial Data
Yi Yang, Xuran Zhao, H. Charles Zhao +5
Recently there has been growing interest in the use of aerial and satellite map data for autonomous vehicles, primarily due to its potential for significant cost reduction and enha…
Exploring Real World Map Change Generalization of Prior-Informed HD Map Prediction Models
Samuel M. Bateman, Ning Xu, H. Charles Zhao +4
Building and maintaining High-Definition (HD) maps represents a large barrier to autonomous vehicle deployment. This, along with advances in modern online map detection models, has…