Delivery services may be able to overcome snow, rain, heat and the gloom of night, but a new class of legged robots is not far behind. Artificial intelligence algorithms developed by a Berkeley team — working with researchers from Facebook and Carnegie Mellon University — are equipping legged robots with an enhanced ability to adapt to and navigate unfamiliar terrain in real time.
Their test robot successfully traversed sand, mud, hiking trails, tall grass and dirt piles without falling. It also outperformed alternative systems in adapting to a weighted backpack thrown onto its top or to slippery, oily slopes. When walking down steps and scrambling over piles of cement and pebbles, it achieved 70% and 80% success rates, respectively. Not only could the robot adjust to novel circumstances, but it could also do so in fractions of a second rather than in minutes or more.
The new AI system, called Rapid Motor Adaptation (RMA), combines a base policy — the algorithm by which the robot determines how to move — with an adaptation module. The base policy uses reinforcement learning to develop controls for sets of extrinsic variables in the environment. Because the robot’s onboard sensors cannot directly measure all possible variables in the environment, the adaptation module directs the robot to teach itself about its surroundings using information based on its own body movements. For example, if a robot senses that its feet are extending farther, it may surmise that the surface is soft and will adapt its next movements accordingly.“Our insight is that change is ubiquitous, so from day one, the RMA policy assumes that the environment will be new,” said Jitendra Malik, professor of electrical engineering and computer sciences. “It’s not an afterthought, but a forethought. That’s our secret sauce.”
Learn more: New AI strategy enables robots to rapidly adapt to real-world environments