Learning Force-Reactive Humanoid Loco-Manipulation

Sihang Li; Xiaofeng Guo; Jinkun Cao; Charlie Cheng; Ruihan Yang; Chen Feng; Guanya Shi; Haozhi Qi

cs.ROOct 26, 2026

Learning Force-Reactive Humanoid Loco-Manipulation

Authors: Sihang Li, Xiaofeng Guo, Jinkun Cao, Charlie Cheng, Ruihan Yang, Chen Feng, Guanya Shi, Haozhi Qi

Organizations: Amazon FAR (Frontier AI & Robotics) · New York University · Carnegie Mellon University · University of Chicago

Abstract

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 trajectories alone. Therefore, we present FLORE, a framework for learning force-reactive humanoid loco-manipulation. We collect robot-free demonstrations using two force-sensing UMI-style handheld grippers for hand motion and contact wrenches, and wearable trackers for body motion. A force-reactive diffusion policy takes vision, proprioception, and measured wrenches as input, then predicts actions and per-hand force commands. A hybrid compliance whole-body controller with force tracking executes these commands while maintaining balance: an integral of the force error measured by real force/torque (F/T) sensors lets each hand track force accurately along the commanded direction and remain compliant along the others. In simulation, the controller tracks per-hand force commands of varied magnitude and direction, with little loss in whole-body motion accuracy. Across three contact-rich tasks on the robot, the force-reactive policy succeeds in 80% of rollouts, compared with 26.7% for a position-only policy trained on the same demonstrations and executed by the same controller. For more details, please visit our website: https://louis-leee.github.io/FLoRe/.

Figures & tables

Fig. 1: Overview of our FLoRe framework. (B) Two handheld UMI-style grippers with F/T sensors and three additional trackers record images, motion, and contact force without a robot. (C) A force-reactive diffusion policy takes images, state and measured force as input and outputs keypoint motion and desired force commands at 30 Hz. (A) The hybrid compliance whole-body controller with force tracking converts the force command into a shift of the target along the force direction, so that one RL policy tracks motion and force simultaneously while remaining compliant along other directions.

Fig. 2: Control law of the hybrid compliance controller. Along the force direction, the target $\mathbf{p}^{\mathrm{tgt}}_{h}$ is shifted from the motion reference $\mathbf{p}^{*}_{h}$ into the surface by the force command and the force feedback, until the measured force $\mathbf{f}^{\mathrm{meas}}_{h}$ balances the command. Along the compliance direction, the gripper yields to $\mathbf{f}^{\mathrm{ext}}_{h}$ (dashed gripper).

Fig. 3: Robot-free data-collection interface. Left: the demonstrator holds a handheld device in each hand and wears trackers on the chest and both ankles. Right: each device carries a VIVE tracker, a RealSense D405 wrist camera, and an F/T sensor mounted between the handle and gripper.

Fig. 4: Hybrid compliance whole-body controller with force tracking. Top: in simulation, each hand presses a randomly placed virtual plane while the robot tracks the reference motion and maintains balance. The arrows show commanded force (pink), measured reaction force (blue), external force (yellow), and force perturbation (red). Bottom: on the real robot, a person pulls both hands, which yield instead of resisting.

ROOT-90 [ 38 ]
		Motion Tracking			Force Tracking †
Controller	$\mathbf{f}^{\mathrm{cmd}}$	Global Root (m) $\downarrow$	Wrist Pos (mm) $\downarrow$	Wrist Ori (rad) $\downarrow$	$a_{1}$ $\rightarrow$ 1	$\|e_{F}\|$ (N) $\downarrow$	$F{=}0$ floor (N) $\downarrow$
SONIC [ 3 ]	✗	0.317	47.16	0.2787	N/A	N/A	N/A
SONIC + admittance [ 3 ]	✓	0.317	47.16	0.2787	0.70	7.89	2.62
FLoRe w/o Force	✗	0.342	36.75	0.2239	N/A	N/A	N/A
FLoRe ( Ours )	✓	0.374	44.70	0.1176	1.06	1.05	1.50

TABLE I: Sim-to-sim evaluation of motion and force tracking for our unified force–position controller and the baselines. Motion tracking is evaluated on two held-out motion sets. † Averaged over the left and right hands.

Fig. 5: Rollouts on the robot and failure modes of the position-only baseline. Top: Ours walks the gantry forward from left to right. The baseline either fails to move the gantry or pushes unequally, causing it to drift from its heading. The traces show commanded (dashed) and measured (solid) force for each hand and axis. Bottom left: Ours drags and rotates the box. The baseline either cannot move it or leans into it and loses balance. Bottom right: Ours erases the strip under steady pressure. The baseline hovers above the board and never makes contact.

	Force Switches			Success Rate (%) $\uparrow$				Evaluation Error (Val) $\downarrow$
Policy	F-in	F-out	F-cmd	push_gantry	rotate_box	wipe_board	Mean	Pose (cm)	Force (N)	Torque (N m)
Ours	✓	✓	✓	7/10	9/10	8/10	80%	1.18	1.29	0.148
w/o F-cmd †	✓	✓	✗	3/10	4/10	4/10	36.7%	1.18	1.29	0.148
w/o F-in	✗	✓	✓	6/10	7/10	7/10	66.7%	1.78	2.51	0.277
DP	✗	✗	✗	2/10	3/10	3/10	26.7%	2.23	N/A	N/A

TABLE II: Success rate and offline validation error of the predicted pose, force and torque for Ours and its force-pathway ablations, trained on the same demonstrations and executed by the same unified controller.

Humanoid Loco-Manipulation

Humanoid loco-manipulation studies how robots coordinate walking, balance, and whole-body interaction to move and handle objects.

See all 93 papers

Dataset-Free Compliant Humanoid Loco-Manipulation with Dynamic Online Posture
Oct 5, 2026Seungho Yeom, Zhenyu Wu, Jaeyoung Huh +4Whole-Body Robotic Manipulation Compliant Robot Control
University of California San Diego, La Jolla, CA 92093, USA. · Yonsei University, Seoul 03722, Republic of Korea.
HULK: Learning Whole-Body Forceful Loco-Manipulation for Humanoids
Oct 6, 2026An Dang, Arturo Flores Alvarez, Yu-Ming Chen +5Humanoid Loco-Manipulation Whole-Body Control
Amazon · University of Michigan · University of California, Los Angeles +1
LAC: Linear and Angular Compliance for Humanoid Whole-body Control
Aug 26, 2026Yang Liu, Zhongkai Gu, Wei Zhu +1Humanoid Robot Control Whole-Body Control
Neuro-Robotics Laboratory, Tohoku University, Japan

Oct 5, 2026J/K move · Enter open · S save

Dataset-Free Compliant Humanoid Loco-Manipulation with Dynamic Online Posture

Seungho Yeom, Zhenyu Wu, Jaeyoung Huh, Diego Williams+3

University of California San Diego, La Jolla, CA 92093, USA. · Yonsei University, Seoul 03722, Republic of Korea.

Whole-Body Robotic Manipulation Compliant Robot Control Humanoid Loco-Manipulation Robotic Control Humanoid Robot Control Robotic Loco-Manipulation+1

Open paper AskPDF arXiv

Most humanoid loco-manipulation controllers require human motion data to learn whole-body coordination and posture, leaving policies reliant on external sources to provide this data. We present OCLO (Online-posture Compliant LOco-manipulation), a humanoid loco-manipulation system trained without human motion data and commanded only through two end-effector targets. Because these targets do not uniquely determine whole-body posture, OCLO generates pelvis height and torso orientation online using an analytic reachability prior, further refined through policy-in-the-loop sampling with a task-agnostic cost. OCLO also learns whole-body compliance by displacing end-effector references according to measured forces through a spring-damper model, encouraging the legs, waist, and pelvis to yield to external loads. In simulation, using the reachability prior leads to a 77.8% success rate in acquiring the commanded reference, a vast improvement over the 37.8% success rate accomplished without the prior. Further, refinement reduces end-effector orientation error across all evaluated tasks. The same posture module improves a pretrained SONIC controller on four of five tasks. Without compliance training, policies tend to lose balance under disturbances rather than sacrifice tracking. On a Unitree G1, OCLO maintains balance under end-effector disturbances that cause its ablations to fail and performs seven loco-manipulation tasks, including crouched walking and picking up a box from a low surface. Project website: https://oclo-humanoid.github.io/

Abstract

Humanoid Loco-Manipulation

Explore similar work

Dataset-Free Compliant Humanoid Loco-Manipulation with Dynamic Online Posture

HULK: Learning Whole-Body Forceful Loco-Manipulation for Humanoids

LAC: Linear and Angular Compliance for Humanoid Whole-body Control