52 citations · 172 across the 27 of their papers we have counts for
28 papers
LIBERO-MAX: Do Robot Policies Adapt When the World Changes?
Yunbei Zhang, Zijian Jin, Yuanzhe Liu +14
Robots must often continue a task after a target moves, the viewpoint shifts, or an obstacle appears, even though their earlier observations and committed actions reflect the previ…
OPRIDE: Offline Preference-based Reinforcement Learning via In-Dataset Exploration
Yiqin Yang, Hao Hu, Yihuan Mao +10
Preference-based reinforcement learning (PbRL) can help avoid sophisticated reward designs and align better with human intentions, showing great promise in various real-world appli…
Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning
Huihan Liu, Changyeon Kim, Bo Liu +2
Continual learning is a long-standing challenge in robot policy learning, where a policy must acquire new skills over time without catastrophically forgetting previously learned on…
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…
Differentiable Information Enhanced Model-Based Reinforcement Learning
Xiaoyuan Zhang, Xinyan Cai, Bo Liu +4
Differentiable environments have heralded new possibilities for learning control policies by offering rich differentiable information that facilitates gradient-based methods. In co…
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…
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…
Grasp Multiple Objects with One Hand
Yuyang Li, Bo Liu, Yiran Geng +5
The intricate kinematics of the human hand enable simultaneous grasping and manipulation of multiple objects, essential for tasks such as object transfer and in-hand manipulation.…
LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
Bo Liu, Yifeng Zhu, Chongkai Gao +4
Lifelong learning offers a promising paradigm of building a generalist agent that learns and adapts over its lifespan. Unlike traditional lifelong learning problems in image and te…
Benchmarking Reinforcement Learning Techniques for Autonomous Navigation
Zifan Xu, Bo Liu, Xuesu Xiao +2
Deep reinforcement learning (RL) has brought many successes for autonomous robot navigation. However, there still exists important limitations that prevent real-world use of RL-bas…
Learning a Shield from Catastrophic Action Effects: Never Repeat the Same Mistake
Shahaf S. Shperberg, Bo Liu, Peter Stone
Agents that operate in an unknown environment are bound to make mistakes while learning, including, at least occasionally, some that lead to catastrophic consequences. When humans…
Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings
Kingsley Nweye, Bo Liu, Peter Stone +1
Building upon prior research that highlighted the need for standardizing environments for building control research, and inspired by recently introduced challenges for real life re…
Conflict-Averse Gradient Descent for Multi-task Learning
Bo Liu, Xingchao Liu, Xiaojie Jin +2
The goal of multi-task learning is to enable more efficient learning than single task learning by sharing model structures for a diverse set of tasks. A standard multi-task learnin…
Team Orienteering Coverage Planning with Uncertain Reward
Bo Liu, Xuesu Xiao, Peter Stone
Many municipalities and large organizations have fleets of vehicles that need to be coordinated for tasks such as garbage collection or infrastructure inspection. Motivated by this…
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition
Bo Liu, Qiang Liu, Peter Stone +3
In real-world multi-agent systems, agents with different capabilities may join or leave without altering the team's overarching goals. Coordinating teams with such dynamic composit…
APPL: Adaptive Planner Parameter Learning
Xuesu Xiao, Zizhao Wang, Zifan Xu +5
While current autonomous navigation systems allow robots to successfully drive themselves from one point to another in specific environments, they typically require extensive manua…
Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey
Xuesu Xiao, Bo Liu, Garrett Warnell +1
Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated navigati…
APPLI: Adaptive Planner Parameter Learning From Interventions
Zizhao Wang, Xuesu Xiao, Bo Liu +2
While classical autonomous navigation systems can typically move robots from one point to another safely and in a collision-free manner, these systems may fail or produce suboptima…
APPLR: Adaptive Planner Parameter Learning from Reinforcement
Zifan Xu, Gauraang Dhamankar, Anirudh Nair +5
Classical navigation systems typically operate using a fixed set of hand-picked parameters (e.g. maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-t…
Extended Abstract: Motion Planners Learned from Geometric Hallucination
Xuesu Xiao, Bo Liu, Peter Stone
Learning motion planners to move robot from one point to another within an obstacle-occupied space in a collision-free manner requires either an extensive amount of data or high-qu…
Machine versus Human Attention in Deep Reinforcement Learning Tasks
Sihang Guo, Ruohan Zhang, Bo Liu +4
Deep reinforcement learning (RL) algorithms are powerful tools for solving visuomotor decision tasks. However, the trained models are often difficult to interpret, because they are…
Agile Robot Navigation through Hallucinated Learning and Sober Deployment
Xuesu Xiao, Bo Liu, Peter Stone
Learning from Hallucination (LfH) is a recent machine learning paradigm for autonomous navigation, which uses training data collected in completely safe environments and adds numer…