Humanoid Parkour Learning
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:MNFBD4KSrecord.jsonopen to challenge →
read the original abstract
Parkour is a grand challenge for legged locomotion, even for quadruped robots, requiring active perception and various maneuvers to overcome multiple challenging obstacles. Existing methods for humanoid locomotion either optimize a trajectory for a single parkour track or train a reinforcement learning policy only to walk with a significant amount of motion references. In this work, we propose a framework for learning an end-to-end vision-based whole-body-control parkour policy for humanoid robots that overcomes multiple parkour skills without any motion prior. Using the parkour policy, the humanoid robot can jump on a 0.42m platform, leap over hurdles, 0.8m gaps, and much more. It can also run at 1.8m/s in the wild and walk robustly on different terrains. We test our policy in indoor and outdoor environments to demonstrate that it can autonomously select parkour skills while following the rotation command of the joystick. We override the arm actions and show that this framework can easily transfer to humanoid mobile manipulation tasks. Videos can be found at https://humanoid4parkour.github.io
This paper has not been read by Pith yet.
Forward citations
Cited by 15 Pith papers
-
Unleashing Infinite Motion: Scaling Expressive Quadrupedal Motion via Generative Video Priors
Uni-Mo generates 7,488 language-annotated quadruped motions via LLM prompts and video diffusion, lifts them to 3D trajectories, and trains policies achieving 96.7% real-robot success on 392 sampled motions.
-
OmniContact: Chaining Meta-Skills via Contact Flow for Generalizable Humanoid Loco-Manipulation
OmniContact introduces contact flow as a shared representation of body trajectories and contact signals to learn and chain loco-manipulation meta-skills, reporting 98.7% success on box carrying and 76.5% on push-stack tasks.
-
Perceptive Behavior Foundation Model: Adapting Human Motion Priors to Robot-Centric Terrain
Perceptive BFM grounds human motion priors in robot terrain perception via terrain-conformal reference synthesis and teacher-student transfer from adapted to raw-reference tracking.
-
Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching
A modular system uses motion matching to compose long-horizon human skill chains, trains RL experts, and distills them into a depth-based policy that lets a Unitree G1 humanoid autonomously climb, vault, and roll over...
-
HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control
HUSKY combines humanoid-skateboard dynamics modeling with adversarial motion priors and physics-guided lean-to-steer strategies to achieve real-world stable skateboarding on a humanoid robot.
-
Humanoid Whole-Body Badminton via Multi-Stage Reinforcement Learning
A multi-stage RL curriculum produces a unified whole-body controller enabling humanoid robots to sustain badminton rallies in simulation and return shuttles at up to 19.1 m/s in real hardware, with both EKF-based and ...
-
OMG: Omni-Modal Motion Generation for Generalist Humanoid Control
OMG is a diffusion model for omni-modal whole-body humanoid motion generation that uses language, audio, and reference motions after large-scale data curation to achieve state-of-the-art performance and adaptation.
-
LadderMan: Learning Humanoid Perceptive Ladder Climbing
A hybrid motion-tracking and imitation-reinforcement pipeline produces a depth-based visuomotor policy that lets humanoids climb varied ladders zero-shot on hardware and perform teleoperated manipulation while climbing.
-
M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking
M3imic unifies heterogeneous motion modalities via encoders into a shared latent space for a single RL-trained whole-body controller achieving high sim success and sim-to-real transfer on Unitree G1.
-
CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation
CoRe-MoE uses a two-stage RL framework with contrastive reweighting in a Mixture-of-Experts architecture to enable gait transitions and multi-terrain adaptation for humanoid locomotion.
-
Bridging the Gap: Enabling Soft Actor Critic for High Performance Legged Locomotion
Targeted changes to policy initialization, critic targets, and return estimation let SAC match PPO performance across legged locomotion tasks in massively parallel simulation.
-
Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy
Terrain-consistent reference modulation during RL training yields SE(2)-controllable humanoid locomotion policies that improve tracking in simulation and enable over 70 m closed-loop autonomous navigation on rough ter...
-
RPG: Robust Policy Gating for Smooth Multi-Skill Transitions in Humanoid Fighting
RPG trains a single policy with transition and timing randomization for stable multi-skill fighting on humanoids, integrated with locomotion for arbitrary-duration combat.
-
TACT-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion
A multi-channel terrain affordance reward combined with lower-body compliance training via virtual wrenches enables end-to-end PPO-trained humanoid policies to walk at 1 m/s on 0.2 m risers with improved payload robustness.
-
RPG: Robust Policy Gating for Smooth Multi-Skill Transitions in Humanoid Fighting
RPG trains a unified humanoid robot policy using motion and temporal randomization to achieve smooth, stable transitions between fighting skills and locomotion.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.