Challenges and issues faced in building a framework for conducting research in learning from observation

We are exploring how primitives, small units of behavior, can speed up robot learning and enable robots to learn difficult dynamic tasks in reasonable amounts of time. In this chapter we describe work on learning from observation and learning from practice on air hockey and marble maze tasks. We discuss our research strategy, results, and open issues and challenges. Primitives are units of behavior above the level of motor or muscle commands. There have been many proposals for such units of behavior in neuroscience, psychology, robotics, artificial intelligence and machine learning (Arkin, 1998; Schmidt, 1988; Schmidt, 1975; Russell and Norvig, 1995; Barto and Mahadevan, 2003). There is a great deal of evidence that biological systems have units of behavior above the level of activating individual motor neurons, and that the organization of the brain reflects those units of behavior (Loeb, 1989). We know that in human eye movement, for example, there are only a few types of movements including saccades, smooth pursuit, vestibular ocular reflex (VOR), optokinetic nystagmus (OKN) and vergence, that general eye movements are generated as sequences of these behavioral units, and that there are distinct brain regions dedicated to generating and controlling each type of eye movement (Carpenter, 1988). We know that there are discrete locomotion patterns, or gaits, for animals with legs (McMahon, 1984). Whether there are corresponding units of behavior for upper limb movement in humans and other primates is not yet clear.