Anticipation and initiative in human-humanoid interaction

One of the long-term goals for humanoid robotics is to have these robots working side-by side with humans, helping the humans in a variety of open ended tasks, which can change in real-time. In such contexts a crucial component of the robot behavior will be to adapt as rapidly as possible to regularities that can be learned from the human. This will allow the robot to anticipate predictable events, in order to render the interaction more fluid. This will be particularly pertinent in the context of tasks that will be repeated several times, or that contain sub-tasks that will be repeated within the global task. Through exposure to repetition the robot should automatically extract and exploit the underlying regularities. Here we present results from human-robot cooperation experiments in the context of a cooperative assembly task. The architecture is characterized by the maintenance and use of an ldquointeraction historyrdquo - a literal record of all past interactions that have taken place. During on-line interaction, the system continuously searches the interaction history for sequences whose onset matches the actions that are currently being invoked. Recognition of such matches allows the robot to take different levels of anticipatory activity. As predicted sequences are successively validated by the user, the level of anticipation and learning increases. Level 1 anticipation allows the system to predict what the user will say, and thus eliminate the need for verification when the prediction holds. At Level 2 allows the system to take initiative to propose the predicted next event. At Level 3, the robot is highly confident and takes initiative to perform the predicted action. We demonstrate how these progressive levels render the cooperative interaction more fluid and more rapid. Implications for further refinement in the quality of human-robot cooperation are discussed.