2 citations · 2 across the 3 of their papers we have counts for
4 papers
A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Yunhai Feng, Natalie Leung, Jiaxuan Wang +3
Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learni…
A Smooth Sea Never Made a Skilled SAILOR: Robust Imitation via Learning to Search
Arnav Kumar Jain, Vibhakar Mohta, Subin Kim +5
The fundamental limitation of the behavioral cloning (BC) approach to imitation learning is that it only teaches an agent what the expert did at states the expert visited. This mea…
Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation
Yunhai Feng, Jiaming Han, Zhuoran Yang +3
Solving complex long-horizon robotic manipulation problems requires sophisticated high-level planning capabilities, the ability to reason about the physical world, and reactively c…
Finetuning Offline World Models in the Real World
Yunhai Feng, Nicklas Hansen, Ziyan Xiong +2
Reinforcement Learning (RL) is notoriously data-inefficient, which makes training on a real robot difficult. While model-based RL algorithms (world models) improve data-efficiency…