10 papers
Proprioceptive Sketches as Long-Horizon Intent for Generative Action Policies
Fangyuan Wang, Songhao Huang, Haoxiang Sun +5
Generative robot policies predict short action chunks but lack explicit long-horizon intent. Recent methods expose longer-horizon structure through language plans, subgoal images,…
Phase-and-First-Arrival VLM Feedback for Sparse-Reward Reinforcement Learning in Surgical Manipulation
Wanli Liuchen, Fangyuan Wang, Bin Li +4
Sparse outcome feedback limits what robots can learn from unsuccessful attempts at complex manipulation. Failed multi-stage surgical attempts can contain grasps, lifts, or transfer…
Dressing in Motion: A Human Motion-Aware Diffusion Policy for Robot-Assisted Dressing
Haoxiang Sun, Fangyuan Wang, Songhao Huang +4
Robotic dressing assistance is a promising solution for supporting older adults with physical impairments in daily living. However, dressing under human motion remains challenging,…
PRIMAL3: Pathfinding via Reinforcement and Imitation Multi-Agent Learning - Leveraging LaCAM3
Chengyang He, Tanishq Duhan, Gadiel Sznaier Camps +6
We present PRIMAL3, an ultra-large-scale learning-based framework for multi-agent pathfinding (MAPF) that integrates reinforcement learning, topology-aware communication, LaCAM3-gu…
World Models for Robotic Manipulation: A Survey
Fangyuan Wang, Ziyuan Wang, Guorui Pei +15
Robotic manipulation depends on the ability to anticipate how actions reshape objects, contacts, and scene geometry before execution. Learned world models provide this capability b…
Think Proprioceptively: State-Grounded Visual Token Selection for VLA Policies
Fangyuan Wang, Peng Zhou, Jiaming Qi +4
Vision-language-action (VLA) models typically inject proprioception only as a late conditioning signal, preventing robot state from grounding instruction understanding or directing…
Phy-Tac: Toward Human-Like Grasping via Physics-Conditioned Tactile Goals
Shipeng Lyu, Lijie Sheng, Fangyuan Wang +5
Humans naturally grasp objects with minimal level required force for stability, whereas robots often rely on rigid, over-squeezing control. To narrow this gap, we propose a human-i…
HuBE: Cross-Embodiment Human-like Behavior Execution for Humanoid Robots
Shipeng Lyu, Fangyuan Wang, Weiwei Lin +3
Achieving both behavioral similarity and appropriateness in human-like motion generation for humanoid robot remains an open challenge, further compounded by the lack of cross-embod…
Instruction-Augmented Long-Horizon Planning: Embedding Grounding Mechanisms in Embodied Mobile Manipulation
Fangyuan Wang, Shipeng Lyu, Peng Zhou +3
Enabling humanoid robots to perform long-horizon mobile manipulation planning in real-world environments based on embodied perception and comprehension abilities has been a longsta…
Explicit-Implicit Subgoal Planning for Long-Horizon Tasks with Sparse Reward
Fangyuan Wang, Anqing Duan, Peng Zhou +4
The challenges inherent in long-horizon tasks in robotics persist due to the typical inefficient exploration and sparse rewards in traditional reinforcement learning approaches. To…