no code implementations • 19 Mar 2024 • Lucy Xiaoyang Shi, Zheyuan Hu, Tony Z. Zhao, Archit Sharma, Karl Pertsch, Jianlan Luo, Sergey Levine, Chelsea Finn
In this paper, we make the following observation: high-level policies that index into sufficiently rich and expressive low-level language-conditioned skills can be readily supervised with human feedback in the form of language corrections.
no code implementations • 26 Jul 2023 • Lucy Xiaoyang Shi, Archit Sharma, Tony Z. Zhao, Chelsea Finn
AWE can be combined with any BC algorithm, and we find that AWE can increase the success rate of state-of-the-art algorithms by up to 25% in simulation and by 4-28% on real-world bimanual manipulation tasks, reducing the decision making horizon by up to a factor of 10.
no code implementations • 15 Jul 2022 • Lucy Xiaoyang Shi, Joseph J. Lim, Youngwoon Lee
From this intuition, we propose a Skill-based Model-based RL framework (SkiMo) that enables planning in the skill space using a skill dynamics model, which directly predicts the skill outcomes, rather than predicting all small details in the intermediate states, step by step.
Model-based Reinforcement Learning reinforcement-learning +1