64 citations · 76 across the 10 of their papers we have counts for
10 papers
PriorVLA: Prior-Preserving Adaptation for Vision-Language-Action Models
Xinyu Guo, Bin Xie, Wei Chai +4
Large-scale pretraining has made Vision-Language-Action (VLA) models promising foundations for generalist robot manipulation, yet adapting them to downstream tasks remains necessar…
USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robots
Junwen Gu, Zhiheng Wu, Pengxuan Si +8
Underwater environments pose unique challenges for robotic navigation and manipulation. While existing research has primarily focused on task-specific methods, studies on general-p…
Efficient Sim-to-real Transfer of Contact-Rich Manipulation Skills with Online Admittance Residual Learning
Xiang Zhang, Changhao Wang, Lingfeng Sun +3
Learning contact-rich manipulation skills is essential. Such skills require the robots to interact with the environment with feasible manipulation trajectories and suitable complia…
Prim-LAfD: A Framework to Learn and Adapt Primitive-Based Skills from Demonstrations for Insertion Tasks
Zheng Wu, Wenzhao Lian, Changhao Wang +3
Learning generalizable insertion skills in a data-efficient manner has long been a challenge in the robot learning community. While the current state-of-the-art methods with reinfo…
Reinforcement learning with Demonstrations from Mismatched Task under Sparse Reward
Yanjiang Guo, Jingyue Gao, Zheng Wu +2
Reinforcement learning often suffer from the sparse reward issue in real-world robotics problems. Learning from demonstration (LfD) is an effective way to eliminate this problem, w…
Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning
Zheng Wu, Yichen Xie, Wenzhao Lian +5
Humans are capable of abstracting various tasks as different combinations of multiple attributes. This perspective of compositionality is vital for human rapid learning and adaptio…
Offline-Online Learning of Deformation Model for Cable Manipulation with Graph Neural Networks
Changhao Wang, Yuyou Zhang, Xiang Zhang +5
Manipulating deformable linear objects by robots has a wide range of applications, e.g., manufacturing and medical surgery. To complete such tasks, an accurate dynamics model for p…
Learning Dense Rewards for Contact-Rich Manipulation Tasks
Zheng Wu, Wenzhao Lian, Vaibhav Unhelkar +2
Rewards play a crucial role in reinforcement learning. To arrive at the desired policy, the design of a suitable reward function often requires significant domain expertise as well…
Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference
Liting Sun, Zheng Wu, Hengbo Ma +1
In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important. Human behavior is naturally rich and diverse. Co…
Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving
Zheng Wu, Liting Sun, Wei Zhan +2
In the past decades, we have witnessed significant progress in the domain of autonomous driving. Advanced techniques based on optimization and reinforcement learning (RL) become in…