MASkillBlender is a multi-agent reinforcement learning framework that enables decentralized coordination of multiple humanoid robots for loco-manipulation tasks. It learns a shared high-level policy that blends pre-trained single-humanoid skills, requiring only task-level rewards and no task-specific motion references. The approach includes a permutation-based data augmentation technique that preserves policy-gradient direction, and it has been evaluated on several coordination tasks across two humanoid embodiments, consistently achieving strong performance.
By Yifan Hu, Luhang Hong, Mingkang Long, Danning Wang, Chengfeng Jia, Rong Su, Junjie Fu, Guanghui Wen
arXiv:2603.03768v2 Announce Type: replace-cross
Abstract: Full-stack human-robot collaboration (HRC) can become brittle when replacing a planner, partner model, coordination policy, or controller cha...
By Hao Zhang, Yisen Li, Ruize Geng, Yves Tseng, Yaru Niu, Ding Zhao, H. Eric Tseng
Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them. But how does this recipe transfer to dexterous manipulation?
CoHuB (Collaborative Multi‑Humanoid Benchmark) is a simulation benchmark designed to evaluate multi‑humanoid collaboration using egocentric visual observations. It includes ten tasks—eight involving two humanoids and two involving three—covering a range of collaboration patterns. The benchmark also supplies synchronized demonstrations collected via a multi‑operator VR teleoperation pipeline, where each operator controls one humanoid from its own egocentric view, and preliminary experiments with visuomotor policies highlight significant challenges in coordinated perception and control.
arXiv:2607. 11874v1 Announce Type: cross Abstract: Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them.
By Yunhai Feng, Natalie Leung, Jiaxuan Wang, Lujie Yang, Haozhi Qi, Preston Culbertson
arXiv:2609.25351v1 Announce Type: cross
Abstract: We focus on human-robot collaborative transport, a challenging task of broad relevance spanning logistics, manufacturing, and the home, in which a us...
By Elvin Yang, Christoforos Mavrogiannis
Humanoid loco-manipulation is often simplified into a stop-and-go process: walking to an object, stopping to manipulate it, and then resuming locomotion. It also commonly relies on low degree-of-freedom (DoF) end effectors that behave like an open-close grasp primitive.
We focus on human-robot collaborative transport, a challenging task of broad relevance spanning logistics, manufacturing, and the home, in which a user and a robot work together to relocate a large or...
arXiv:2606. 12352v1 Announce Type: cross Abstract: Multi-robot collaboration allows robots to efficiently take on a wide range of tasks, from moving a couch through a doorway to assembling structures on a construction site.
By Ria Doshi, Tian Gao, Annie Chen, Chelsea Finn, Jeannette Bohg
Learning humanoid-object interaction requires coordinating whole-body balance, locomotion, and dexterous hand contact to control both robot and object motion. Human demonstrations provide examples of...
arXiv:2510. 08807v2 Announce Type: replace-cross Abstract: From loco-motion to dextrous manipulation, humanoid robots have made remarkable strides in demonstrating complex full-body capabilities.
By Zhenyu Zhao, Hongyi Jing, Xiawei Liu, Jiageng Mao, Abha Jha, Hanwen Yang, Rong Xue, Sergey Zakharov, Vitor Guizilini, Yue Wang
arXiv:2603. 03751v2 Announce Type: replace-cross Abstract: Cooperative object transport in unstructured environments remains challenging for assistive humanoids because strong, time-varying interaction forces can make tracking-centric whole-body control unreliable, especially in close-contact support tasks.
By Hao Zhang, Yves Tseng, Ding Zhao, H. Eric Tseng