9 papers
HALO: Learning Human-Robot Collaboration via Heterogeneous-Agent Lyapunov Policy Optimization
Hao Zhang, Yaru Niu, Yikai Wang +2
To improve generalization and resilience in human-robot collaboration (HRC), robots must contend with diverse combinations of human behaviors and contexts, motivating multi-agent r…
Learning Versatile Humanoid Manipulation with Touch Dreaming
Yaru Niu, Zhenlong Fang, Binghong Chen +8
Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stability, end-effector dexterity,…
Dexterous Manipulation Policies from RGB Human Videos via 3D Hand-Object Trajectory Reconstruction
Hongyi Chen, Tony Dong, Tiancheng Wu +7
Multi-finger robotic hand manipulation and grasping are challenging due to the high-dimensional action space and the difficulty of acquiring large-scale training data. Existing app…
Unifying Agent Interaction and World Information for Multi-agent Coordination
Dongsu Lee, Daehee Lee, Yaru Niu +3
This work presents a novel representation learning framework, *interaction-world* latent (IWoL), to facilitate *team coordination* in multi-agent reinforcement learning (MARL). Bui…
Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining
Yaru Niu, Yunzhe Zhang, Mingyang Yu +11
Quadrupedal robots have demonstrated impressive locomotion capabilities in complex environments, but equipping them with autonomous versatile manipulation skills in a scalable way…
Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing
Yuming Feng, Chuye Hong, Yaru Niu +6
Recently, quadrupedal locomotion has achieved significant success, but their manipulation capabilities, particularly in handling large objects, remain limited, restricting their us…