3 citations · 11 across the 15 of their papers we have counts for
15 papers
Rank2Reward: Learning Shaped Reward Functions from Passive Video
Daniel Yang, Davin Tjia, Jacob Berg +3
Teaching robots novel skills with demonstrations via human-in-the-loop data collection techniques like kinesthetic teaching or teleoperation puts a heavy burden on human supervisor…
Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback
Max Balsells, Marcel Torne, Zihan Wang +3
Ideally, we would place a robot in a real-world environment and leave it there improving on its own by gathering more experience autonomously. However, algorithms for autonomous ro…
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
Chuning Zhu, Max Simchowitz, Siri Gadipudi +1
Visual model-based RL methods typically encode image observations into low-dimensional representations in a manner that does not eliminate redundant information. This leaves them s…
Lifelong Robot Learning with Human Assisted Language Planners
Meenal Parakh, Alisha Fong, Anthony Simeonov +3
Large Language Models (LLMs) have been shown to act like planners that can decompose high-level instructions into a sequence of executable instructions. However, current LLM-based…
Universal Visual Decomposer: Long-Horizon Manipulation Made Easy
Zichen Zhang, Yunshuang Li, Osbert Bastani +4
Real-world robotic tasks stretch over extended horizons and encompass multiple stages. Learning long-horizon manipulation tasks, however, is a long-standing challenge, and demands…
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets
Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik +6
Offline policy learning is aimed at learning decision-making policies using existing datasets of trajectories without collecting additional data. The primary motivation for using r…