Using hierarchical generative models to enhance the motor control of autonomous robots
To best move in their surrounding environment and tackle everyday tasks, robots should be able to perform complex motions, effectively coordinating the movement of individual limbs. Roboticists and computer scientists have thus been trying to develop computational techniques that can artificially replicate the process through which humans plan, execute, and coordinate the movements of different body parts.
Robots are increasingly being used in manufacturing, agriculture and health care. But programming a team of robots to carry out individual tasks raises a question: How can robots learn from other robots if they are built differently? A multi-institutional team including Chongjie Zhang, an associate professor of computer science and…
Today's robots are stuck—their bodies are usually closed systems that can neither grow nor self-repair, nor adapt to their environment. Now, scientists at Columbia University have developed robots that can physically "grow," "heal," and improve themselves by integrating material from their environment or from other robots.
Home robots could assist humans with the completion of various chores and manual tasks, ranging from washing dishes or doing the laundry to cooking, cleaning and tidying up. While many roboticists and computer scientists have tried to improve the skills of home robots in recent years, many of the robots…