1 citations · 2 across the 13 of their papers we have counts for
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UniIntervene++: An Adaptive Intervention Agent for Efficient Real-World Reinforcement Learning
Yudong Lin, Haoyuan Deng, Zhuoxuan Yuan +3
Online reinforcement learning (RL) enables robot policies to improve through physical interaction, but the assistance they require changes as their competence evolves. Existing int…
Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation
Haoyuan Deng, Haichao Liu, Wenkai Guo +6
Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We present Facet-0, a robotic foundat…
AffordTrajDP: Dynamic Affordance-Guided Visuomotor Policy Learning for Robotic Manipulation
Gaoyuan Wu, Ziyu Shan, Haoyang Du +2
Affordance-guided imitation learning has shown impressive performance in robotic manipulation tasks by compressing visual perception into task-specific geometric constraints (e.g.,…
UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning
Haoyuan Deng, Yitong Gao, Yudong Lin +3
Human-in-the-loop reinforcement learning (HiL-RL) has emerged as an effective paradigm for real-world robotic manipulation, enabling online policy improvement with human guidance.…
DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation
Ziyu Shan, Yuheng Zhou, Gaoyuan Wu +3
Mobile manipulation is a fundamental capability that enables robots to interact in expansive environments such as homes and factories. Most existing approaches follow a two-stage p…
UniPR: Unified Object-level Real-to-Sim Perception and Reconstruction from a Single Stereo Pair
Chuanrui Zhang, Yingshuang Zou, ZhengXian Wu +3
Perceiving and reconstructing objects from images are critical for real-to-sim transfer tasks, which are widely used in the robotics community. Existing methods rely on multiple su…
E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning
Haoyuan Deng, Yudong Lin, Yuanjiang Xue +5
Human-in-the-loop guidance has emerged as an effective approach for accelerating online reinforcement learning (RL) in real-world manipulation. However, existing human-in-the-loop…