arXiv Machine Learning
Ctrl-F: Force as an Interface for Humanoid Loco-Manipulation
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
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.
Hugging Face Trending Papers
Oct 26
Learning Force-Reactive Humanoid Loco-Manipulation
Many everyday tasks require controlling contact force as well as motion. On a humanoid, this force must often be coordinated across both hands and the whole body, and cannot be specified by kinematic...
arXiv AI
Sep 4
FWBC-VLA: Force-Aware Whole-Body Compensation for Contact-Rich Loco-Manipulation
FWBC‑VLA is a force‑aware framework that links vision‑language‑action (VLA) models with whole‑body compensation control for wheeled‑legged robots. It introduces HSR‑Force, a sensorless residual‑torque estimator that infers contact strength and encodes this information as tokens for the VLA action decoder, allowing the policy to detect contact onset, loading, and release. The system fine‑tunes a pretrained VLA backbone on a large WL&Arm dataset, combines proprioceptive, Jacobian‑derived force, and contact estimates to generate corrective actions, and demonstrates effectiveness in real‑world tasks such as whiteboard wiping and door opening.
Hugging Face Trending Papers
Jun 22
CoorDex: Coordinating Body and Hand Priors for Continuous Dexterous Humanoid Loco-Manipulation
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.
arXiv AI
Oct 26
DexPort: A Hand-Held Skill Interface for Dexterous Hand Data Collection
DexPort is a portable interface that lets operators collect high‑quality dexterous hand demonstrations without a full robot setup. By using the same hand hardware for both collection and deployment, it records joint targets and states while allowing operators to specify wrist motion and select from a small set of coordinated finger skills. The authors collected over 2,500 demonstrations across four real‑world tasks, showing significant performance gains when using recorded robot commands and in‑the‑wild data.
arXiv AI
Sep 23
Touch2Robot: Robot Touch in the Human Demonstration Loop
arXiv:2609.24660v2 Announce Type: cross Abstract: Human demonstrations offer a scalable way to collect manipulation data, but their contacts may be unstable or infeasible when transferred to a robot...