A reinforcement learning system enabled a four-legged robot to amble over unfamiliar, rapidly changing terrain.
What’s new: Researchers at UC Berkeley, Facebook, and Carnegie Mellon developed Rapid Motor Adaptation (RMA). The system enabled a Unitree Robotics A1 to negotiate changing conditions and unexpected obstacles nearly in real time. The machine traversed muddy trails, bushy backcountry, and an oil-slicked plastic sheet without falling.
How it works: The system includes two algorithms, both of which are trained in simulation. The reinforcement learning component learns to control locomotion basics, while the adaptation module learns to generate a representation of the environment.
- In deployment, the two algorithms run asynchronously on a single edge device. They analyze the previous 0.5 seconds of data from limbs and joints and adjust the gait accordingly.
- In tests, the robot maneuvered through conditions that it hadn’t encountered in simulations, such as a squishy foam mattress, over piles of rubble, and rough-hewn staircases. It repeated many of the tests carrying loads of varying weight.
- The machine achieved 70 percent or better success in each scenario. When it fell, the mishap typically was due to a sudden drop while descending stairs or debris that blocked more than one leg.
Behind the news: Video clips of robots from Boston Dynamics and others have become viral sensations in recent years. They may be mouth-watering, but the bots involved often are programmed for specific motions or scenarios and can’t adapt to novel conditions.
Why it matters: RMA is among the first robotic walking systems that don’t need to be trained for every variety of terrain they're likely to encounter.
We’re thinking: For many applications where navigating flat ground is sufficient, wheeled locomotion is much simpler and more reliable. But legs still carry the day when navigating rough terrain — not to discount their uncanny anthropomorphic appeal. They’re likely to be important for tasks like fighting fires, traversing disaster zones, and navigating the toy-strewn obstacle course that is Andrew’s daughter's playroom.