Hugging Face Trending Papers
Learning Force-Reactive Humanoid Loco-Manipulation
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arXiv AI
Oct 27
Learning Force-Reactive Humanoid Loco-Manipulation
The paper introduces FLORE, a framework for learning force‑reactive humanoid loco‑manipulation. It uses robot‑free demonstrations with force‑sensing grippers and wearable trackers to train a diffusion policy that predicts actions and per‑hand force commands from vision, proprioception, and measured wrenches. A hybrid compliance whole‑body controller then executes these commands, achieving accurate force tracking and maintaining balance, outperforming a position‑only policy on three contact‑rich tasks.
arXiv Machine Learning
2d ago
Ctrl-F: Force as an Interface for Humanoid Loco-Manipulation
arXiv:2611.05507v1 Announce Type: cross Abstract: Humanoid motion tracking has scaled to general policies that reproduce diverse human motion, but the resulting behavior is in free-space: the tracker...
Hugging Face Trending Papers
Sep 15
Weave: Learning Whole-Body Dexterous Loco-Manipulation from Human-Object Interactions
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 AI
Sep 16
Weave: Learning Whole-Body Dexterous Loco-Manipulation from Human-Object Interactions
arXiv:2609.16683v1 Announce Type: cross Abstract: Learning humanoid-object interaction requires coordinating whole-body balance, locomotion, and dexterous hand contact to control both robot and objec...
arXiv AI
Aug 26
ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching
ForceFlow is a force-aware reactive framework that uses flow matching to improve contact-rich manipulation. It fuses force signals asymmetrically, treats force as a global regulator, and employs a joint prediction paradigm to couple force and motion. The approach splits tasks into a vision-dominant localization stage and a touch-dominant execution stage, using a Vision-to-Force handover to separate spatial generalization from contact regulation.
arXiv AI
Jul 14
A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
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.