Lockheed Martin Robotics Seminar: Yiannis Aloimonos, "Robots learning action plans"
Friday, February 27, 2015
2121 JM Patterson
Lockheed Martin Robotics Seminar
Robots Learning Action Plans by Watching YouTube Videos
Professor, Computer Science and UMIACS
Affiliate Faculty, ISR
Autonomous robots will need to learn the actions that humans perform. They will need to recognize these actions when they see them and they will need to perform these actions themselves. In this presentation, which is given in the form of a Socratic dialogue, it is proposed that this learning task can be achieved using the manipulation grammar.
Context-free grammars have been in fashion in linguistics because they provide a simple and precise mechanism for describing the methods by which phrases in some natural language are built from smaller blocks. Also, the basic recursive structure of natural languages, the way in which clauses nest inside other clauses, and the way in which lists of adjectives and adverbs are followed by nouns and verbs, is described exactly. Similarly, for manipulation actions, every complex activity is built from smaller blocks involving hands and their movements, as well as objects, tools and the monitoring of their state. Thus, interpreting a “seen” action is like understanding language, and executing an action from knowledge in memory is like producing language. Several experiments will be shown robots interpreting and executing human actions in the arts and crafts or kitchen domain.
Professor Aloimonos holds a Ph.D. in Computer Science from the University of Rochester.
His research is devoted to the principles governing the design and analysis of real-time systems that possess perceptual capabilities, for the purpose of both explaining animal vision and designing seeing machines. Such capabilities have to do with the ability of the system to control its motion and the motion of its parts using visual input (navigation and manipulation) and the ability of the system to break up its environment into a set of categories relevant to its tasks and recognize these categories (categorization and recognition).