The Interactive Robotics Group aims to imagine the future of work by designing collaborative robot teammates that enhance human capability.
We design models and algorithms that enable robots to infer human cognitive state and learn implicit constraints and preferences of human team members from listening, watching, and practicing/training with human teams. Through this process, robots and machines learn “unwritten rules of the game” for working with teams of people in complex organizations and can collaborate to strengthen team plans. We also develop algorithms that enable robots to make fast adjustments to team plans in response to disturbances — to “play the game” with people. This work has been translated to enable new forms of human-machine teaming in manufacturing assembly lines, healthcare applications, transportation, and defense.
Most robots are programmed using one of two methods: learning from demonstration, in which they watch a task being done and then replicate it, or via motion-planning techniques such as optimization or sampling, which require a programmer to explicitly specify a task’s goals and constraints.
Every other year, the International Conference on Automated Planning and Scheduling hosts a competition in which computer systems designed by conference participants try to find the best solution to a planning problem, such as scheduling flights or coordinating tasks for teams of autonomous satellites. On all but the most straightforward problems, however, even the best planning algorithms still aren’t as effective as human beings with a particular aptitude for problem-solving — such as MIT students.
Today’s robots are awkward co-workers because they are often unable to predict what humans need. In hospitals, robots are employed to perform simple tasks such as delivering supplies and medications, but they have to be explicitly told what to do. A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) thinks that this will soon change, and that robots might be most effective by helping humans perform one of the most complex tasks of all: scheduling. In a pair of new papers, CSAIL researchers demonstrate a robot that, by learning from human workers, can help assign and schedule tasks in fields ranging from medicine to the military.
Autonomous robots performing a joint task send each other continual updates: “I’ve passed through a door and am turning 90 degrees right.” “After advancing 2 feet I’ve encountered a wall. I’m turning 90 degrees right.” “After advancing 4 feet I’ve encountered a wall.” And so on.Computers, of course, have no trouble filing this information away until they need it. But such a barrage of data would drive a human being crazy.
Computers are good at identifying patterns in huge data sets. Humans, by contrast, are good at inferring patterns from just a few examples.In a paper appearing at the Neural Information Processing Society’s conference next week, CSAIL researchers present a new system that bridges these two ways of processing information, so that humans and computers can collaborate to make better decisions.
