6 papers
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…
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…
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…
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…