Terrain-Indexed Motion Priors and Geometric Support for Extreme Humanoid Parkour
Overview
This paper presents a progressive imitation framework that learns terrain-appropriate humanoid parkour skills and adapts hand-knee support. Using demonstrations, the method employs privileged imitation for executable references, terrain-indexed adversarial motion priors for skill acquisition, and geometric support guidance for contact adaptation. On the Noetix E1, the policy achieves 96.34% hybrid success in simulation and transfers without real-world fine-tuning, completing 29 of 30 obstacle trials and four of five hybrid courses.
Generated from attributed reports · Updated 2 days ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“on, and velocity commands without an external skill label. On the Noetix E1, the policy achieves 96.34\% hybrid success in simulation and transfers without real-world policy fine-tuning, completing 29 of 30 individual obstacle trials and four of five hybrid courses. Support-adaptation ablations show higher traversal success for the complete adaptation stage on the tested terrains, while qualitative observations show”
Exact source · revision 1Source owner not reported
Reported quantity · trajectories: 29 other · reported trials · Differing source assertions
“on, and velocity commands without an external skill label. On the Noetix E1, the policy achieves 96.34\% hybrid success in simulation and transfers without real-world policy fine-tuning, completing 29 of 30 individual obstacle trials and four of five hybrid courses. Support-adaptation ablations show higher traversal success for the complete adaptation stage on the tested terrains, while qualitative observations show”
Exact source · revision 1Source owner not reported
Reported quantity · trajectories: 5 other · reported trials · Differing source assertions
“on, and velocity commands without an external skill label. On the Noetix E1, the policy achieves 96.34\% hybrid success in simulation and transfers without real-world policy fine-tuning, completing 29 of 30 individual obstacle trials and four of five hybrid courses. Support-adaptation ablations show higher traversal success for the complete adaptation stage on the tested terrains, while qualitative observations show”
Exact source · revision 1Source owner not reported
Reported quantity · trajectories: 30 other · reported trials · Differing source assertions
“on, and velocity commands without an external skill label. On the Noetix E1, the policy achieves 96.34\% hybrid success in simulation and transfers without real-world policy fine-tuning, completing 29 of 30 individual obstacle trials and four of five hybrid courses. Support-adaptation ablations show higher traversal success for the complete adaptation stage on the tested terrains, while qualitative observations show”
Exact source · revision 1Source owner not reported
Reported quantity · trajectories: 4 other · reported trials · Differing source assertions
“on, and velocity commands without an external skill label. On the Noetix E1, the policy achieves 96.34\% hybrid success in simulation and transfers without real-world policy fine-tuning, completing 29 of 30 individual obstacle trials and four of five hybrid courses. Support-adaptation ablations show higher traversal success for the complete adaptation stage on the tested terrains, while qualitative observations show”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalTerrain-Indexed Motion Priors and Geometric Support for Extreme Humanoid Parkour
This paper presents a progressive imitation framework that learns terrain-appropriate humanoid parkour skills and adapts hand-knee support. Using demonstrations, the method employs privileged imitation for executable references, terrain-indexed adversarial motion priors for skill acquisition, and geometric support guidance for contact adaptation. On the Noetix E1, the policy achieves 96.34% hybrid success in simulation and transfers without real-world fine-tuning, completing 29 of 30 obstacle trials and four of five hybrid courses.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).