Learning Agile Humanoid Dodgeball with Onboard Monocular Vision
Overview
This research presents a system for humanoid robots to perform agile dodgeball maneuvers using onboard monocular vision. The approach integrates visual threat assessment with whole-body evasion and recovery policies. A three-stage curriculum transitions the robot from privileged inputs to estimator outputs, utilizing analytic projection and perception randomization for training. Results show high success rates in simulation (97.8-100.0% for 5.0 m launches at 6 m/s) and over 85% success on a physical Unitree G1 across more than 15 trials.
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Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · throws per condition: 1000 other · Basis not reported
“rom stable policy rollouts, and finally replaces the actor's privileged inputs with estimator outputs. Analytic projection and perception randomization provide detector-like training observations without image rendering. Across approach-angle bins in controlled simulation, dodge success ranges from 97.8--100.0% for 5.0 m launches at 6 m/s to 33.2--40.8% for 2.5 m launches at 10 m/s, with 1000 throws per speed--distan”
Exact source · revision 1Source owner not reported
Reported quantity · trials for single-throw success: 15 other · reported trials
“ce condition. On a physical Unitree G1, controlled single-throw success exceeds 85% over more than 15 trials. A hardware sequence demonstrates full-azimuth target reacquisition followed by another dodge. These results demonstrate an integrated onboard-monocular-vision system under highly dynamic motions.”
Exact source · revision 1Source owner not reported
Reported quantity · training throws per condition: 1000 other · Basis not reported
“rom stable policy rollouts, and finally replaces the actor's privileged inputs with estimator outputs. Analytic projection and perception randomization provide detector-like training observations without image rendering. Across approach-angle bins in controlled simulation, dodge success ranges from 97.8--100.0% for 5.0 m launches at 6 m/s to 33.2--40.8% for 2.5 m launches at 10 m/s, with 1000 throws per speed--distan”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalLearning Agile Humanoid Dodgeball with Onboard Monocular Vision
This research presents a system for humanoid robots to perform agile dodgeball maneuvers using onboard monocular vision. The approach integrates visual threat assessment with whole-body evasion and recovery policies. A three-stage curriculum transitions the robot from privileged inputs to estimator outputs, utilizing analytic projection and perception randomization for training. Results show high success rates in simulation (97.8-100.0% for 5.0 m launches at 6 m/s) and over 85% success on a physical Unitree G1 across more than 15 trials.
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