{"ID":3083891,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T05:16:48.22291569Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05873","arxiv_id":"2606.05873","title":"LadderMan: Learning Humanoid Perceptive Ladder Climbing","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 .","short_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 en...","url_abs":"https://arxiv.org/abs/2606.05873","url_pdf":"https://arxiv.org/pdf/2606.05873v1","authors":"[\"Siheng Zhao\",\"Yuanhang Zhang\",\"Ziqi Lu\",\"Pieter Abbeel\",\"Rocky Duan\",\"Koushil Sreenath\",\"Yue Wang\",\"C. Karen Liu\",\"Guanya Shi\"]","published":"2026-06-04T08:47:08Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.CV\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}