2 citations · 4 across the 8 of their papers we have counts for
8 papers
Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning
Jiaheng Hu, Jay Shim, Chen Tang +4
Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that can adapt in openended, evolving…
L3M+P: Lifelong Planning with Large Language Models
Krish Agarwal, Yuqian Jiang, Jiaheng Hu +2
By combining classical planning methods with large language models (LLMs), recent research such as LLM+P has enabled agents to plan for general tasks given in natural language. How…
Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks
Viraj Joshi, Zifan Xu, Bo Liu +2
Multi-task Reinforcement Learning (MTRL) has emerged as a critical training paradigm for applying reinforcement learning (RL) to a set of complex real-world robotic tasks, which de…
Learning Memory Mechanisms for Decision Making through Demonstrations
William Yue, Bo Liu, Peter Stone
In Partially Observable Markov Decision Processes, integrating an agent's history into memory poses a significant challenge for decision-making. Traditional imitation learning, rel…
Fine-Grained Gradient Restriction: A Simple Approach for Mitigating Catastrophic Forgetting
Bo Liu, Mao Ye, Peter Stone +1
A fundamental challenge in continual learning is to balance the trade-off between learning new tasks and remembering the previously acquired knowledge. Gradient Episodic Memory (GE…
Longhorn: State Space Models are Amortized Online Learners
Bo Liu, Rui Wang, Lemeng Wu +3
Modern large language models are built on sequence modeling via next-token prediction. While the Transformer remains the dominant architecture for sequence modeling, its quadratic…
t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making
William Yue, Bo Liu, Peter Stone
Deep generative replay has emerged as a promising approach for continual learning in decision-making tasks. This approach addresses the problem of catastrophic forgetting by levera…
Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning
Bo Liu, Yihao Feng, Qiang Liu +1
Goal-conditioned reinforcement learning (GCRL) has a wide range of potential real-world applications, including manipulation and navigation problems in robotics. Especially in such…