8 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…
X-Morph: Human Motion Priors for Scalable Robot Learning Across Morphologies
Ritwik Sharma, Shivam Sood, Arhaan Jain +3
Recent progress in humanoid behavior models has been driven in large part by abundant human motion data, but comparable motion data is scarce for non-humanoid legged robots such as…
FALCON: Actively Decoupled Visuomotor Policies for Loco-Manipulation with Foundation-Model-Based Coordination
Chengyang He, Ge Sun, Yue Bai +3
We present FoundAtion-model-guided decoupled LoCO-maNipulation visuomotor policies (FALCON), a framework for loco-manipulation that combines modular diffusion policies with a visio…
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning
Mingliang Zhang, Sichang Su, Chengyang He +1
In multi-agent reinforcement learning (MARL), achieving multi-task generalization to diverse agents and objectives presents significant challenges. Existing online MARL algorithms…
SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding
Shuhao Liao, Weihang Xia, Yuhong Cao +4
The Multi-Agent Path Finding (MAPF) problem aims to determine the shortest and collision-free paths for multiple agents in a known, potentially obstacle-ridden environment. It is t…