9 papers · 1 filter
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…
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…
MAP-VLA: Memory-Augmented Prompting for Vision-Language-Action Model in Robotic Manipulation
Runhao Li, Wenkai Guo, Zhenyu Wu +5
Pre-trained Vision-Language-Action (VLA) models have achieved remarkable success in improving robustness and generalization for end-to-end robotic manipulation. However, these mode…
MoTo: A Zero-shot Plug-in Interaction-aware Navigation for General Mobile Manipulation
Zhenyu Wu, Angyuan Ma, Xiuwei Xu +5
Mobile manipulation stands as a core challenge in robotics, enabling robots to assist humans across varied tasks and dynamic daily environments. Conventional mobile manipulation ap…