Light-Loco-Parkour: Versatile Perceptive Whole-Body Locomotion via Multi-Skill Distillation

2026-08-01

AI Summary

This work addresses the challenge of achieving human-like agile locomotion for humanoid robots in complex terrains, where existing approaches often suffer from limited generalization or an inability to exploit whole-body responses online. The authors propose an end-to-end perception-driven whole-body motion control strategy that relies solely on onboard depth images and velocity commands—without requiring reference trajectories, skill labels, or handcrafted state machines—to autonomously execute walking, balancing, climbing, and stepping-over behaviors. Their approach establishes a perception-to-control pipeline that generates terrain-adaptive, dynamically feasible references from sparse action seeds and enables label-free skill switching through reward-driven learning. A single policy uniformly handles both open-terrain navigation and obstacle traversal, achieving high success rates in simulation and real-world experiments, and demonstrates zero-shot transfer to indoor and outdoor hardware platforms, thereby validating the feasibility of fully onboard-perception-driven autonomous whole-body locomotion in outdoor environments.

Abstract

Existing humanoid whole-body control systems still fall short of the way humans move through cluttered terrain: they either track expressive whole-body references without terrain generalization, or react to terrain online while leaving the arms, torso, and knees largely unused. We present \texttt{Light-Loco-Parkour} (LLP), an end-to-end perceptive whole-body locomotion system that closes this gap with a single deployable policy. Conditioned only on onboard depth and a velocity command, the policy decides when to walk, balance, climb, step down, or vault, with no reference input, skill label, hand-coded gate, or runtime motion graph. Compared with prior humanoid systems, LLP makes three contributions. First, it introduces a whole-body perceptive-control pipeline that extends an RL-trained, velocity-tracking locomotion policy with parkour skills learned from object-interacting motions, so the same policy tracks velocity in open terrain, executes whole-body traversal at obstacles, and resumes locomotion afterward. Second, it acquires terrain-conditioned skills from sparse seeds by expanding a single motion into dynamically feasible, terrain-paired references across obstacle geometry, rather than relying on a large motion corpus. Third, it learns autonomous skill transitions from reward, letting the policy decide when and which whole-body skill to invoke from depth and command alone, with no one-hot skill label, hand-coded state machine, or runtime motion generator. Simulation and real-world experiments show high success across both benchmarked terrains and unseen obstacle variations, and the same policy transfers zero-shot to indoor and outdoor hardware experiments. These results demonstrate autonomous perceptive whole-body locomotion on a humanoid in outdoor settings, using only onboard sensing and a single deployable policy.

Problem

• humanoid locomotion
• whole-body control
• terrain generalization
• perceptive locomotion
• obstacle traversal

Innovation

• whole-body locomotion
• perceptive control
• multi-skill distillation
• autonomous skill transition
• terrain-conditioned policy