LadderMan: Learning Humanoid Perceptive Ladder Climbing

• URL: http://arxiv.org/abs/2606.05873v1
• Date: Thu, 04 Jun 2026 08:47:08 GMT
• Title: LadderMan: Learning Humanoid Perceptive Ladder Climbing
• Abstract summary: We present textbfLadderMan, a unified system that enables humanoid robots to robustly climb diverse ladders.<n>Our climbing policy is built on a scalable two-stage learning pipeline, where we use hybrid motion tracking to learn multiple climbing experts.<n>We further train a separate manipulation policy using a dual-agent formulation, allowing stable on-ladder manipulation via teleoperation.
• Score: 56.550077812088404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humanoid robots hold great promise for operating in human-centered environments, yet ladder climbing remains one of the most challenging tasks due to sparse footholds and handholds, complex whole-body coordination, and sensitivity to perception and control errors. We present \textbf{LadderMan}, a unified system that enables humanoid robots to robustly climb diverse ladders and perform manipulation under such constrained conditions. Our climbing policy is built on a scalable two-stage learning pipeline, where we use hybrid motion tracking to learn multiple climbing experts from a single reference motion, and distill these experts into a unified depth-based visuomotor climbing policy via hybrid imitation and reinforcement learning. To enable real-world deployment, we leverage vision foundation models to bridge the sim-to-real gap in depth perception. Building on the learned climbing policy, we further train a separate manipulation policy using a dual-agent formulation, allowing stable on-ladder manipulation via teleoperation. Experiments demonstrate that LadderMan achieves robust ladder climbing across a wide range of geometries, successfully transfers to real-world hardware in a zero-shot manner, and supports various manipulation tasks under challenging ladder constraints. Video results are available at https://ladderman-robot.github.io .

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