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[2602.15827] Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching
Summary
The paper presents a framework for humanoid robots to perform dynamic parkour using motion matching and reinforcement learning, enabling agile navigation in complex environments.
Why It Matters
This research addresses the challenge of replicating human-like agility in robotics, which is crucial for applications in real-world navigation and obstacle avoidance. By enabling robots to autonomously adapt to their surroundings, this work has implications for advancements in robotics, AI, and human-robot interaction.
Key Takeaways
- Introduces Perceptive Humanoid Parkour (PHP) framework for agile robot navigation.
- Utilizes motion matching for composing complex human-like movements.
- Integrates perception-driven decision-making for real-time obstacle interaction.
- Demonstrates successful real-world applications with a Unitree G1 humanoid robot.
- Advances the field of robotics by enhancing dynamic skill execution.
Computer Science > Robotics arXiv:2602.15827 (cs) [Submitted on 17 Feb 2026] 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 View a PDF of the paper titled Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching, by Zhen Wu and 11 other authors View PDF HTML (experimental) 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-track...
