Modeling manipulation interactions by hidden Markov models

This paper describes a new approach on how to teach everyday manipulation tasks to a robot under the "Learning from Observation" framework. In our previous work, to acquire low-level action primitives of a task automatically, we proposed a technique to estimate essential interactions to complete a task by integrating multiple observations of similar demonstrations. But after many demonstrations are performed, there are possibly interactions which are the same in nature. These identical interactions should be grouped so that each action primitive becomes unique. For this purpose, a Hidden Markov Model based clustering algorithm is presented which automatically determines the number of the independent interactions. We also show that obtained interactions can be used as discriminators of human behavior. Finally, a simulation result and an experimental result in which a real humanoid robot learns and recognizes essential actions by observing demonstrations are presented.