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This paper presents asRoBallet, a humanoid ballbot, and demonstrates successful zero-shot Sim2Real transfer of reinforcement learning policies for complex maneuvers. They achieve this by developing a high-fidelity MuJoCo simulation that explicitly models the discrete roller mechanics of omni-wheels, capturing previously ignored parasitic vibrations and contact discontinuities. The Friction-Aware RL framework masters coupled rolling, lateral, and torsional friction channels, enabling the transfer to hardware.
Zero-shot Sim2Real transfer for a humanoid ballbot is now possible thanks to a friction-aware RL framework and high-fidelity simulation that models omni-wheel mechanics.
We introduce asRoBallet, to the best of our knowledge, the first successful deployment of reinforcement learning (RL) on a humanoid ballbot hardware. Historically, ballbots have served as a canonical benchmark for underactuated and nonholonomic control, which are characterized by a reality gap in complex friction models for wheel-sphere-ground interactions. While current literature demonstrates successful handling of 3D balancing with LQR and MPC, transitioning to actual hardware for a humanoid ballbot using RL is currently hindered by critical gaps in contact modeling, actuator latency&jitter, and safe hardware exploration, and safe hardware exploration. This study proposes a high-fidelity MuJoCo simulation that explicitly models the discrete roller mechanics of ETH-type omni-wheels, thereby capturing parasitic vibrations and contact discontinuities that are previously ignored. We also developed a Friction-Aware Reinforcement Learning framework that achieves zero-shot Sim2Real transfer by mastering the coupled rolling, lateral, and torsional friction channels at the wheel-sphere and sphere-ground interfaces. We designed asRoBallet through subtractive reconfiguration, repurposing key components from an overconstrained quadruped and integrating them into a newly designed structural frame to achieve a robust research platform at low cost. We also developed a generalized iOS ecosystem that transforms consumer electronics into a low-latency interface, enabling a single operator to orchestrate expressive humanoid maneuvers via intuitive natural motion.
