# FORGE: Force-Aware Multi-Granularity Control for Efficient Humanoid Loco-Manipulation

**Authors:** Liangjun Xing, Ke Fan, Kailin Li, Bin Wang, Xiaojie Niu, Qingwei Ben, Jiafei Cao, Wei Yao, Yebin Liu, Jingbo Wang, Weinan Zhang  
**Published:** 12 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.17142) · [PDF](https://arxiv.org/pdf/2611.17142) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.17142.html)

## Abstract

Humanoid loco-manipulation requires coordinated locomotion, balance regulation, and manipulation under frequent contacts, making teleoperated demonstration collection difficult: a practical controller must simultaneously provide precision, stability, and contact awareness. We propose FORGE, a force-aware multi-granularity control system with a coarse-to-fine command space spanning velocity navigation, planar foot placement, wrist-pose tracking, and optional force-conditioning inputs. Policy training requires no full-body human motion trajectories or joint-angle references; deployment consumes only sparse bilateral wrist poses relative to the pelvis, coarse motion commands, and optional desired wrist-load inputs. For sustained contact, FORGE combines a momentum observer with a learned correction trained using simulator contact-force supervision. At deployment, it estimates low-frequency, wrist-equivalent interaction forces from proprioception and reconstructed low-level arm PD torques, without wrist force/torque sensors. A quasi-static virtual-target loop uses this estimate and an optional desired applied-force input to adapt the wrist target. A frozen locomotion prior and asymmetric residual actions stabilize manipulation-induced whole-body corrections. Experiments evaluate unified-control stability, task-space tracking, quasi-static wrist-equivalent force estimation, squeezing forces during sustained contact with a rigid box, and downstream diffusion-policy execution of a squat-and-lift task across payload conditions. Website: https://bb0928.github.io/forge/.

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