AI & ML
Ctrl-F: Force as an Interface for Humanoid Loco-Manipulation
arXiv cs.RO0 views
arXiv:2611.05507v1 Announce Type: new Abstract: Humanoid motion tracking has scaled to general policies that reproduce diverse human motion, but the resulting behavior is in free-space: the tracker knows where the body should be, not how it should interact with the environment. Other work makes the object part of the objective, yet abstracts that interaction to contact or geometry, which leaves the demanding cases under-explored. We argue that force is a natural command channel between motion imitation and object interaction, one that specifies not just where to make contact but how hard. We present Ctrl-F, a hierarchical motion-force framework that augments motion tracking with an explicit force command, a 3-D force at each designated contact body such as a hand or a foot. The hierarchical framework comprises: (1) A unified motion-force controller tracks the reference motion while applying the commanded force to the world. (2) A high-level force planner that issues a force command online based on object state and proprioception observations. In simulation and on hardware, Ctrl-F completes loco-manipulation that demands large, sustained interaction force and adjusts its command online as the load changes, both out of reach of a force-blind tracker. On hardware, Ctrl-F demonstrates hand-based dragging, pushing, and lifting, as well as foot-based kicking, successfully manipulating boxes weighing up to 20kg in pushing and kicking tasks where the force-blind baseline fails, while adapting online to additional loads introduced mid-task.
Source:https://arxiv.org/abs/2611.05507PreviousJev with Foresight: Safe Language-Guided Autonomous Driving via a Language World ModelNextMESA: An Evaluation Framework for Compositional, Semantic, and Spatial Generalization in Robotics
Related posts
The Multipath Reliable Connection (MRC) Transport
0arXiv cs.NI
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators
0arXiv cs.NI
Spatial Path Superposition: A No-Cloning Perspective on MIMO-Inspired Spatial Diversity
0arXiv cs.NI
Optimization of Joint Beamforming in RIS-aided Multi-carrier and Multi-user MIMO Systems
0arXiv cs.NI
Energy-efficient End-to-End Communication System with Spiking Neural Networks
0arXiv cs.NI
PAID: Phoneme-Aligned Acoustic Units for Cross-Corpus L1-Background Classification
0arXiv eess.AS