4 citations · 4 across the 11 of their papers we have counts for
12 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,…
SMaRT-Tug: Structured Multi-Agent Reinforcement Learning for Physics-Based Tugboat-Barge Collaborative Manipulation
Junkai Lu, Jiadong Zhao, Jiacheng Zhang +8
Autonomous tugboating is central for automating maritime operations such as port logistics and vessel maneuvering, where multiple tugboats must cooperatively transport/manipulate a…
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…
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…
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…
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…
Latent Theory of Mind: A Decentralized Diffusion Architecture for Cooperative Manipulation
Chengyang He, Gadiel Sznaier Camps, Xu Liu +2
We present Latent Theory of Mind (LatentToM), a decentralized diffusion policy architecture for collaborative robot manipulation. Our policy allows multiple manipulators with their…
Demystifying Diffusion Policies: Action Memorization and Simple Lookup Table Alternatives
Chengyang He, Xu Liu, Gadiel Sznaier Camps +2
Diffusion policies have demonstrated remarkable dexterity and robustness in intricate, high-dimensional robot manipulation tasks, while training from a small number of demonstratio…
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…
Social Behavior as a Key to Learning-based Multi-Agent Pathfinding Dilemmas
Chengyang He, Tanishq Duhan, Parth Tulsyan +2
The Multi-agent Path Finding (MAPF) problem involves finding collision-free paths for a team of agents in a known, static environment, with important applications in warehouse auto…
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…
ALPHA: Attention-based Long-horizon Pathfinding in Highly-structured Areas
Chengyang He, Tianze Yang, Tanishq Duhan +2
The multi-agent pathfinding (MAPF) problem seeks collision-free paths for a team of agents from their current positions to their pre-set goals in a known environment, and is an ess…