Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching

• URL: http://arxiv.org/abs/2602.15827v1
• Date: Tue, 17 Feb 2026 18:59:11 GMT
• Title: Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching
• Authors: Zhen Wu, Xiaoyu Huang, Lujie Yang, Yuanhang Zhang, Koushil Sreenath, Xi Chen, Pieter Abbeel, Rocky Duan, Angjoo Kanazawa, Carmelo Sferrazza, Guanya Shi, C. Karen Liu,
• Abstract summary: We present Perceptive Humanoid Parkour (PHP), a modular framework that enables humanoid robots to autonomously perform long-horizon, vision-based parkour.<n>We train motion-tracking reinforcement learning expert policies for these composed motions, and distill them into a single depth-based, multi-skill student policy.<n>We validate our framework with extensive real-world experiments on a Unitree G1 humanoid robot.
• Score: 77.28042137892943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While recent advances in humanoid locomotion have achieved stable walking on varied terrains, capturing the agility and adaptivity of highly dynamic human motions remains an open challenge. In particular, agile parkour in complex environments demands not only low-level robustness, but also human-like motion expressiveness, long-horizon skill composition, and perception-driven decision-making. In this paper, we present Perceptive Humanoid Parkour (PHP), a modular framework that enables humanoid robots to autonomously perform long-horizon, vision-based parkour across challenging obstacle courses. Our approach first leverages motion matching, formulated as nearest-neighbor search in a feature space, to compose retargeted atomic human skills into long-horizon kinematic trajectories. This framework enables the flexible composition and smooth transition of complex skill chains while preserving the elegance and fluidity of dynamic human motions. Next, we train motion-tracking reinforcement learning (RL) expert policies for these composed motions, and distill them into a single depth-based, multi-skill student policy, using a combination of DAgger and RL. Crucially, the combination of perception and skill composition enables autonomous, context-aware decision-making: using only onboard depth sensing and a discrete 2D velocity command, the robot selects and executes whether to step over, climb onto, vault or roll off obstacles of varying geometries and heights. We validate our framework with extensive real-world experiments on a Unitree G1 humanoid robot, demonstrating highly dynamic parkour skills such as climbing tall obstacles up to 1.25m (96% robot height), as well as long-horizon multi-obstacle traversal with closed-loop adaptation to real-time obstacle perturbations.

Related papers

• Deep Whole-body Parkour [33.232856360240106]
  We present a framework where exteroceptive sensing is integrated into whole-body motion tracking.<n>We demonstrate the non-trivial benefit of integrating perception into the control loop.<n>Results show that this framework enables robust, highly dynamic multi-contact motions, such as vaulting and dive-rolling, on unstructured terrain.
  arXiv  Detail & Related papers  (2026-01-12T16:33:16Z)
• StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion [31.30409161905949]
  StyleLoco is a novel framework for learning humanoid locomotion.<n>It combines the agility of reinforcement learning with the natural fluidity of human-like movements.<n>We demonstrate that StyleLoco enables humanoid robots to perform diverse locomotion tasks.
  arXiv  Detail & Related papers  (2025-03-19T10:27:44Z)
• Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning [54.26816599309778]
  We propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL)<n> Specifically, we introduce a dynamic balance mechanism by leveraging an extended measure of Zero-Moment Point (ZMP)-driven rewards and task-driven rewards in a whole-body actor-critic framework.<n> Experiments conducted on a full-sized Unitree H1-2 robot verify the ability of our method to maintain balance on extremely narrow terrains.
  arXiv  Detail & Related papers  (2025-02-24T14:53:45Z)
• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• Extreme Parkour with Legged Robots [43.041181063455255]
  We show how a single neural net policy operating directly from a camera image can overcome imprecise sensing and actuation. We show our robot can perform a high jump on obstacles 2x its height, long jump across gaps 2x its length, do a handstand and run across tilted ramps.
  arXiv  Detail & Related papers  (2023-09-25T17:59:55Z)
• Robot Parkour Learning [70.56172796132368]
  Parkour is a grand challenge for legged locomotion that requires robots to overcome various obstacles rapidly. We develop a reinforcement learning method inspired by direct collocation to generate parkour skills. We distill these skills into a single vision-based parkour policy and transfer it to a quadrupedal robot using its egocentric depth camera.
  arXiv  Detail & Related papers  (2023-09-11T17:59:17Z)
• Barkour: Benchmarking Animal-level Agility with Quadruped Robots [70.97471756305463]
  We introduce the Barkour benchmark, an obstacle course to quantify agility for legged robots. Inspired by dog agility competitions, it consists of diverse obstacles and a time based scoring mechanism. We present two methods for tackling the benchmark.
  arXiv  Detail & Related papers  (2023-05-24T02:49:43Z)
• Robust and Versatile Bipedal Jumping Control through Reinforcement Learning [141.56016556936865]
  This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled bipedal robot to perform robust and versatile dynamic jumps in the real world. We present a reinforcement learning framework for training a robot to accomplish a large variety of jumping tasks, such as jumping to different locations and directions. We develop a new policy structure that encodes the robot's long-term input/output (I/O) history while also providing direct access to a short-term I/O history.
  arXiv  Detail & Related papers  (2023-02-19T01:06:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.