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FLORE: Learning Force-Reactive Humanoid Loco-Manipulation

The research team proposes the FLORE framework, which uses two force-sensing UMI-style handheld grippers and wearable trackers to collect robot-free demonstrations, and trains a force-reactive diffusion policy that takes vision, proprioception, and measured force wrenches as input and outputs actions and per-hand force commands. A companion hybrid compliant whole-body controller uses integral force error from real force/torque sensors to achieve single-hand force tracking while maintaining balance. In real-robot tests on three contact-rich tasks, the force-reactive policy achieved an 80% success rate, compared with 26.7% for a position-only policy under the same controller.

Part of the event: FLORE: A Force-Reactive Humanoid Mobile Manipulation Framework

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