Boston Dynamics has equipped its Atlas humanoid with wheels, allowing the robot to rapidly roll across flat terrain while still using its articulated legs to navigate obstacles and uneven surfaces. "The 'experiment' combined classical model-based motor control with whole-body reinforcement learning to maintain dynamic stability across unstructured, real-world terrain," reports The Robot Report. From the report: In addition to learning how to move with wheels for feet, Atlas also performs pirouettes and various aggressive actuation maneuvers. This demonstrates that Atlas can smoothly switch between fast rolling on flat terrain and articulated legged motion over obstacles or irregular surfaces.
[Andy Wu, controls engineer for Atlas at Boston Dynamics] trained the system in sim. The system learned all of the behaviors in sim and executed them in real life, with surprising accuracy, according to Wu. Wu said converting Atlas' locomotion style was "wonderfully simple." A single custom adapter mounts an off-the-shelf scooter rim onto Atlas' existing ankle actuator, and that's the only new part in the whole build. Because Atlas was designed from the start to be produced at high volume and to be easy to service, Wu said the retrofit was about as involved as swapping a wheel on a bicycle. The software side was just as straightforward, he said. Each wheel is modeled as an "ankle" that can spin continuously, so all Wu had to do was take the baseline robot model and swap the feet out for wheels.
The next question that Wu intends to answer is the policy response to failed actuators. This is key to safety and to understanding how the system will respond to a system failure. The primary takeaway for Wu and the controls engineering team at Boston Dynamics is to understand control synergy. The video demonstrates that merging classical motor controls (for baseline stability) with whole-body RL (for reactive adaptation) yields superior real-world agility compared to using either method alone.
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