5 citations · 29 across the 29 of their papers we have counts for
6 papers · 1 filter
Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency
Brian Zhu, Momen Khalil, E Harrison +17
While reinforcement learning (RL) allows generalist robot policies to continually improve during deployment, the large model size of modern generalist policies, such as VLAs, poses…
Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?
Filippo Lazzati, Kyle Stachowicz, William Chen +3
Action chunking---predicting and executing multiple actions instead of a single action---has proven to be a critical component for learning effective robotic control policies. Howe…
Adapting Generalist Robot Policies with Semantic Reinforcement Learning
Jagdeep Singh Bhatia, Andrew Wagenmaker, William Chen +1
Generalist robot policies learn a diverse repertoire of behaviors from large-scale pretraining. In principle, this makes them excellent priors for downstream adaptation via reinfor…
Learning Process Rewards via Success Visitation Matching for Efficient RL
Raymond Tsao, Andrew Wagenmaker, Sergey Levine
In many modern applications of reinforcement learning (RL), the natural reward for a task of interest is inherently sparse: a reward of 0 is given everywhere except when the task i…
Improving Robotic Generalist Policies via Flow Reversal Steering
Andy Tang, William Chen, Andrew Wagenmaker +2
Generalist policies can learn a wide range of skills from diverse robot datasets. In order to solve or improve on challenging new tasks, we need a way to infer and invoke the appro…
RoboReward: General-Purpose Vision-Language Reward Models for Robotics
Tony Lee, Andrew Wagenmaker, Karl Pertsch +3
A well-designed reward is critical for effective reinforcement learning-based policy improvement. In real-world robotics, obtaining such rewards typically requires either labor-int…