Prior research on learning from humans often requires real human demonstrations. But advances in video generation models offer a new paradigm: we can now prompt these generators to make synthetic videos of the task. On the plus side, these synthetic videos take place in the robot's current environment, and do not requi...
Yin-Long Dai, Homanga Bharadhwaj, Dylan P. Losey· 0 citations
World models offer a promising alternative to physics-based simulators, yet remain far from practical deployment. We ask how far scaling ego-centric human video takes them, using a dataset of 30,000 hours spanning over 1,000 scene types and 14,000 contributors. Rather than relying on opaque downstream metrics, we direc...
Jia-Hua Dong, Anurag Bagchi, Yash Jangir et al.· 0 citations
Human videos contain rich causal evidence for robot manipulation: they reveal how hand motion induces object motion and produces task-relevant changes in object state. In this work, we study articulated objects such as doors, drawers, cabinets, laptops, ovens, and hinged containers that are ubiquitous in daily life and...
This work studies how to learn anticipation of how objects move during human-object interaction from ordinary monocular videos of human-object interaction, and shows that it can efficiently re-purpose video priors into explicit geometric forecasts for embodied intelligence.
