Sihang Li and coauthors present FLORE for force-reactive humanoid loco-manipulation

Sihang Li and coauthors present FLORE for force-reactive humanoid loco-manipulation

Sihang Li and coauthors present FLORE, a framework for humanoid robots to coordinate movement and contact force during tasks involving physical contact. Its force-reactive diffusion policy uses vision, proprioception, and measured wrenches to predict actions and per-hand force commands; a hybrid compliance whole-body controller executes them while maintaining balance. The authors report that, across three contact-rich robot tasks, the policy succeeded in 80% of rollouts, compared with 26.7% for a position-only policy trained on the same demonstrations and run with the same controller. They also report that, in simulation, the controller tracked force commands of varied magnitude and direction with little loss in whole-body motion accuracy. Escape Meridian’s reading: These results suggest a possible benefit of representing force as well as motion when a humanoid interacts with objects, but the abstract does not establish performance beyond the reported tasks. It does not identify the tasks or report how results vary across conditions. How well does FLORE perform on different contact-rich tasks or under changed conditions?

Publication: 1afc0a31-2573-4d6c-8242-793b74d0c721

attribution — pass: The reading correctly credits FLORE to Sihang Li and coauthors, as listed on the arXiv page. It frames the results as the authors' reports, and the closing inference is labeled as Escape Meridian's reading.

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

summary — uncertain: The core development and results are faithful, but several central terms are left unexplained for a non-specialist: loco-manipulation, diffusion policy, proprioception, rollouts, and especially measured wrenches, which a lay reader may take to mean tools. The reading also omits the robot-free demonstration method, which used handheld force-sensing grippers and wearable trackers. Smallest repair: briefly gloss these terms or attach tooltips, for example wrenches as forces and torques at the hands, and add one clause on how the demonstrations were collected.

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.

date — pass: The originalPublished date of 2026-10-26 matches the v1 submission date on arXiv. The later feed entry on 2026-10-27 is correctly not used.

[Submitted on 26 Oct 2026]

support — pass: Every factual claim matches the abstract, including the 80% versus 26.7% comparison under the same demonstrations and controller, and the simulation force tracking. The interpretation is conditional and labeled, and it does not extend performance beyond the tested tasks. The stated gaps are accurate: the abstract does not name the tasks or report how results vary.

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.

media — pass: The selected media is the verified arXiv abstract page for this paper. It is the direct original source.