Extraction of essential interactions through multiple observations of human demonstrations

This paper describes a new approach on how to teach a robot everyday manipulation tasks under the "Learning from Observation" framework. In this approach, human demonstrations, which are made up of mutual interactions between a grasped object and an environmental object, are observed and a reusable manipulation task model is automatically generated. Most of the similar approaches so far assume that a demonstration can be well understood from a single demonstration. However, a single demonstration contains ambiguity, in that interactions which are essential to complete a task cannot be discerned without prior task dependent knowledge, which should be obtained from observation. To address these issues, a technique to integrate multiple observations of demonstrations is proposed. The demonstrations differ, but are virtually the same task. The shared interactions among all the demonstrations are considered to be essential and a task model is generated from their symbolic representations. Then, the relative trajectories corresponding to each essential interaction are generalized by calculating their mean and variance, and they are also stored in the task model, which is used to reproduce skilled behavior. This approach is examined by using a human-form robot, which successfully imitates human demonstrations of everyday tasks.