9 citations · 11 across the 5 of their papers we have counts for
4 papers · 1 filter
Video World Models Need Predictable Latents
Mattia Scardecchia, Sherry Yang
Video world models trained on large-scale robot data promise a safe and scalable approach for learning and evaluating policies that act in the physical world. However, they still s…
MoCaP: Mobile Code-as-Policies for Zero-Shot Mobile Manipulation from Passive Human Videos
Yiyang Cai, Tatsuya Kamijo, Ninghao Lu +2
Passive human videos of everyday activity offer robots access to a massive amount of human experience without requiring a demonstration for each task. However, using this experienc…
World-Gymnast: Training Robots with Reinforcement Learning in a World Model
Ansh Kumar Sharma, Yixiang Sun, Ninghao Lu +3
Robot learning from interacting with the physical world is fundamentally bottlenecked by the cost of physical interaction. The two alternatives, supervised finetuning (SFT) from ex…
World Models as an Intermediary between Agents and the Real World
Sherry Yang
Large language model (LLM) agents trained using reinforcement learning has achieved superhuman performance in low-cost environments like games, mathematics, and coding. However, th…