1 citations · 2 across the 7 of their papers we have counts for
7 papers
Hierarchical Skill Retrieval for Data-Efficient Adaptation of Vision-Language-Action Models
Haoran Hao, Shahram Najam Syed, Jeff Schneider +1
While Vision-Language-Action (VLA) models pretrained on large-scale robot datasets provide a strong foundation for robot manipulation, their performance can degrade when adapted to…
FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement
Haoran Hao, Shahram Najam Syed, Jeffrey Ichnowski +1
Robot policies inevitably encounter failures when deployed in real environments. Naive retries often repeat the same mistakes, while many existing recovery methods rely on human in…
Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark
Yang Fu, Haomin Bao, Rohit Sonker +4
Offline reinforcement learning (RL) offers a promising route for developing plasma controllers from historical tokamak data, since online trial-and-error on real devices is costly…
Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning
Aravind Venugopal, Jiayu Chen, Xudong Wu +3
The temporal lag between actions and their long-term consequences makes credit assignment a challenge when learning goal-directed behaviors from data. Generative world models captu…
Continual Policy Consolidation for Lifelong Robot Learning
Qijun He, Yuxuan Li, Mingqi Yuan +4
Building a generalist robot policy requires continuously integrating new skills while preserving previously acquired behaviors. Directly optimizing a single policy over a growing t…
Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning
Jiayu Chen, Le Xu, Wentse Chen +1
Offline reinforcement learning (RL) is a powerful approach for data-driven decision-making and control. Compared to model-free methods, offline model-based reinforcement learning (…
ME-IGM: Individual-Global-Max in Maximum Entropy Multi-Agent Reinforcement Learning
Wen-Tse Chen, Yuxuan Li, Shiyu Huang +2
Multi-agent credit assignment is a fundamental challenge for cooperative multi-agent reinforcement learning (MARL), where a team of agents learn from shared reward signals. The Ind…