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RISE: Test-Time Motion Steering for Perceptive Humanoid Fall Recovery

RISE is a perceptive humanoid fall-recovery method that uses task objectives at inference time to steer how the robot stands up. It learns a recovery vocabulary that links intended movements to motor actions through sensory feedback; a visual diffusion planner learns from executions of this vocabulary to predict motion intent as future body states, and task costs steer that prediction toward a desired heading, rise speed, or path, after which motion latents are generated accordingly. Simulations demonstrate diverse recoveries, terrain adaptation, and test-time steering without retraining, with a real-robot demo on the Unitree G1.

Part of the event: RISE: Guiding Humanoid Fall Recovery with Task Objectives at Inference Time

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