Learning to Walk with Rapid Motor Adaptation
How can we train a robot that can generalize to thousands of unseen environments? This question underscores the holy grail of AI research dominated by learning from demonstrations or rewards once in diverse scenarios. However, both of these paradigms fall short because it is difficult to supervise an agent for all possible situations it can encounter in the future. We posit that generalization is truly only possible if the robot can continually and rapidly adapt itself to new situations. This adaptation has to occur online, at a time scale of fractions of a second, which implies that we have no time to carry out multiple experiments in the physical world. In this talk, I will describe a formulation for what we call Rapid Motor Adaptation (RMA) through a case study of legged robots. Legged locomotion is commonly studied and programmed as a discrete set of structured gait patterns, like walk, trot, gallop. However, studies of children learning to walk (Adolph et al) show that real-world locomotion is often quite unstructured and more like "bouts of intermittent steps". We have developed a general approach to walking which is built on learning on varied terrains in simulation followed by rapid online adaptation in the real world. I will show how this setup naturally leads to animal-like gaits in our robot and can be tightly coupled with goal-driven navigation.
Deepak Pathak is a faculty in the School of Computer Science at Carnegie Mellon University. He received his Ph.D. from UC Berkeley and his research spans computer vision, machine learning, and robotics. He is a recipient of the faculty awards from Google, Sony, GoodAI, and graduate fellowship awards from Facebook, NVIDIA, Snapchat. His research has been featured in popular press outlets, including The Economist, The Wall Street Journal, Quanta Magazine, Washington Post, CNET, Wired, and MIT Technology Review among others. Deepak received his Bachelor's from IIT Kanpur with a Gold Medal in Computer Science. He co-founded VisageMap Inc. later acquired by FaceFirst Inc.