9 citations · 9 across the 9 of their papers we have counts for
10 papers
GAINS: Leveraging Inconsistent Human Intervention Signals in Reinforcement Learning
Xinyi Zhang, Yinuo Zhao, Pei Ren +7
Correcting robot manipulation policies through human intervention holds great promise for real-world deployment, yet human operators are inherently imperfect in both the actions th…
Robo-ValueRL: Reliable Value Estimation for Offline-to-Online Reinforcement Learning
Wenke Xia, Pei Ren, Wenbo Yu +10
Offline-to-online reinforcement learning is promising for generalizable robotic manipulation, yet its full-stack complexity obscures reproduction and diagnosis. Within such systems…
RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation
Xinhua Wang, Kun Wu, Zhen Zhao +10
Enhancing the generalization of robotic learning in diverse unseen environments remains a fundamental challenge. Existing approaches often rely on large-scale pretraining, which is…
Real-world Reinforcement Learning from Suboptimal Interventions
Yinuo Zhao, Huiqian Jin, Lechun Jiang +9
Real-world reinforcement learning (RL) offers a promising approach to training precise and dexterous robotic manipulation policies in an online manner, enabling robots to learn fro…
Training-free Generation of Temporally Consistent Rewards from VLMs
Yinuo Zhao, Jiale Yuan, Zhiyuan Xu +6
Recent advances in vision-language models (VLMs) have significantly improved performance in embodied tasks such as goal decomposition and visual comprehension. However, providing a…
ArtVIP: Articulated Digital Assets of Visual Realism, Modular Interaction, and Physical Fidelity for Robot Learning
Zhao Jin, Zhengping Che, Tao Li +10
Robot learning increasingly relies on simulation to advance complex ability such as dexterous manipulations and precise interactions, necessitating high-quality digital assets to b…
HACTS: a Human-As-Copilot Teleoperation System for Robot Learning
Zhiyuan Xu, Yinuo Zhao, Kun Wu +5
Teleoperation is essential for autonomous robot learning, especially in manipulation tasks that require human demonstrations or corrections. However, most existing systems only off…
ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning
Kun Wu, Yinuo Zhao, Zhiyuan Xu +5
Offline Reinforcement Learning (RL), which operates solely on static datasets without further interactions with the environment, provides an appealing alternative to learning a saf…
Efficient Training of Generalizable Visuomotor Policies via Control-Aware Augmentation
Yinuo Zhao, Kun Wu, Tianjiao Yi +5
Improving generalization is one key challenge in embodied AI, where obtaining large-scale datasets across diverse scenarios is costly. Traditional weak augmentations, such as cropp…
CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving
Yinuo Zhao, Kun Wu, Zhiyuan Xu +4
Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors. Widely-applied metho…