LadderMan: Learning Humanoid Perceptive Ladder Climbing

Siheng Zhao, Yuanhang Zhang, Ziqi Lu, Pieter Abbeel, Rocky Duan, Koushil Sreenath, Yue Wang, C. Karen Liu, Guanya Shi · N/A · 2026

Summary

Abstract Summary

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 extbf{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 .

Key Points

Demonstrates humanoid robot control on real hardware
Addresses dexterous manipulation challenges
Uses simulation or synthetic data for training
Uses knowledge distillation to compress models
Leverages 3D geometric reasoning for perception
Incorporates tactile or force feedback for robust interaction
Enables complex whole-body climbing behaviors
Learns multimodal motion skills from human demonstrations

LadderMan: Learning Humanoid Perceptive Ladder Climbing

Authors: Siheng Zhao, Yuanhang Zhang, Ziqi Lu, Pieter Abbeel, Rocky Duan, Koushil Sreenath, Yue Wang, C. Karen Liu, Guanya Shi

Venue: arXiv preprint | Year: 2026

arXiv: 2606.05873v1

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 .

Key Contributions

Demonstrates humanoid robot control on real hardware
Addresses dexterous manipulation challenges
Uses simulation or synthetic data for training
Uses knowledge distillation to compress models
Leverages 3D geometric reasoning for perception
Incorporates tactile or force feedback for robust interaction
Enables complex whole-body climbing behaviors
Learns multimodal motion skills from human demonstrations

Topics

sim-to-real
reinforcement-learning
vision
manipulation
humanoid

Code & Data

No code repository linked in paper metadata.

BibTeX

@article{Zhao2026_260605873v1,
  title={LadderMan: Learning Humanoid Perceptive Ladder Climbing},
  author={Siheng Zhao and Yuanhang Zhang and Ziqi Lu and Pieter Abbeel and Rocky Duan and Koushil Sreenath and Yue Wang and C. Karen Liu and Guanya Shi},
  year={2026},
  eprint={2606.05873v1},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2606.05873v1}
}