17 citations · 45 across the 15 of their papers we have counts for
15 papers
GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation
Yunfei Li, Xiao Ma, Jiafeng Xu +18
We present GR-RL, a robotic learning framework that turns a generalist vision-language-action (VLA) policy into a highly capable specialist for long-horizon dexterous manipulation.…
Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams
Zhi Su, Yuman Gao, Emily Lukas +8
Achieving coordinated teamwork among legged robots requires both fine-grained locomotion control and long-horizon strategic decision-making. Robot soccer offers a compelling testbe…
SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging With Neural Networks Based on Ballistocardiograms
Shuzhen Li, Yuxin Chen, Xuesong Chen +9
Sleep monitoring plays a crucial role in maintaining good health, with sleep staging serving as an essential metric in the monitoring process. Traditional methods, utilizing medica…
Long-horizon Locomotion and Manipulation on a Quadrupedal Robot with Large Language Models
Yutao Ouyang, Jinhan Li, Yunfei Li +4
We present a large language model (LLM) based system to empower quadrupedal robots with problem-solving abilities for long-horizon tasks beyond short-term motions. Long-horizon tas…
Leveraging Symmetry in RL-based Legged Locomotion Control
Zhi Su, Xiaoyu Huang, Daniel Ordoñez-Apraez +8
Model-free reinforcement learning is a promising approach for autonomously solving challenging robotics control problems, but faces exploration difficulty without information of th…
Learning Agile Bipedal Motions on a Quadrupedal Robot
Yunfei Li, Jinhan Li, Wei Fu +1
Can a quadrupedal robot perform bipedal motions like humans? Although developing human-like behaviors is more often studied on costly bipedal robot platforms, we present a solution…
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning
Jiayu Chen, Zelai Xu, Yunfei Li +6
Learning Nash equilibrium (NE) in complex zero-sum games with multi-agent reinforcement learning (MARL) can be extremely computationally expensive. Curriculum learning is an effect…
Grounding Object Relations in Language-Conditioned Robotic Manipulation with Semantic-Spatial Reasoning
Qian Luo, Yunfei Li, Yi Wu
Grounded understanding of natural language in physical scenes can greatly benefit robots that follow human instructions. In object manipulation scenarios, existing end-to-end model…
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration
Chao Yu, Xinyi Yang, Jiaxuan Gao +8
We consider the problem of cooperative exploration where multiple robots need to cooperatively explore an unknown region as fast as possible. Multi-agent reinforcement learning (MA…
Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning
Yunfei Li, Tian Gao, Jiaqi Yang +2
It has been a recent trend to leverage the power of supervised learning (SL) towards more effective reinforcement learning (RL) methods. We propose a novel phasic approach by alter…
Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resets
Yunfei Li, Tao Kong, Lei Li +1
Can a robot autonomously learn to design and construct a bridge from varying-sized blocks without a blueprint? It is a challenging task with long horizon and sparse reward -- the r…
A Benchmark for Low-Switching-Cost Reinforcement Learning
Shusheng Xu, Yancheng Liang, Yunfei Li +2
A ubiquitous requirement in many practical reinforcement learning (RL) applications, including medical treatment, recommendation system, education and robotics, is that the deploye…
Learning to Design and Construct Bridge without Blueprint
Yunfei Li, Tao Kong, Lei Li +2
Autonomous assembly has been a desired functionality of many intelligent robot systems. We study a new challenging assembly task, designing and constructing a bridge without a blue…
Solving Compositional Reinforcement Learning Problems via Task Reduction
Yunfei Li, Yilin Wu, Huazhe Xu +2
We propose a novel learning paradigm, Self-Imitation via Reduction (SIR), for solving compositional reinforcement learning problems. SIR is based on two core ideas: task reduction…
Pretrain Soft Q-Learning with Imperfect Demonstrations
Xiaoqin Zhang, Yunfei Li, Huimin Ma +1
Pretraining reinforcement learning methods with demonstrations has been an important concept in the study of reinforcement learning since a large amount of computing power is spent…