Walk the PLANC: Physics-Guided RL for Agile Humanoid Locomotion on Constrained Footholds

• URL: http://arxiv.org/abs/2601.06286v1
• Date: Fri, 09 Jan 2026 19:56:42 GMT
• Title: Walk the PLANC: Physics-Guided RL for Agile Humanoid Locomotion on Constrained Footholds
• Abstract summary: Bipedal humanoid robots must coordinate balance, timing, and contact decisions when locomoting on constrained footholds.<n>We introduce a locomotion framework in which a reduced-order stepping planner supplies dynamically consistent motion targets.<n>This combination of structured footstep planning and data-driven adaptation produces accurate, agile, and hardware-validated stepping-stone locomotion on a humanoid robot.
• Score: 20.44610499489492
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bipedal humanoid robots must precisely coordinate balance, timing, and contact decisions when locomoting on constrained footholds such as stepping stones, beams, and planks -- even minor errors can lead to catastrophic failure. Classical optimization and control pipelines handle these constraints well but depend on highly accurate mathematical representations of terrain geometry, making them prone to error when perception is noisy or incomplete. Meanwhile, reinforcement learning has shown strong resilience to disturbances and modeling errors, yet end-to-end policies rarely discover the precise foothold placement and step sequencing required for discontinuous terrain. These contrasting limitations motivate approaches that guide learning with physics-based structure rather than relying purely on reward shaping. In this work, we introduce a locomotion framework in which a reduced-order stepping planner supplies dynamically consistent motion targets that steer the RL training process via Control Lyapunov Function (CLF) rewards. This combination of structured footstep planning and data-driven adaptation produces accurate, agile, and hardware-validated stepping-stone locomotion on a humanoid robot, substantially improving reliability compared to conventional model-free reinforcement-learning baselines.

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