Tomikazu Tanuki

Recognition of human task by attention point analysis

This paper presents a novel method of constructing a human task model by attention point (AP) analysis. The AP analysis consists of two steps: at the first step, it broadly observes human task, constructs rough human task model and finds APs which require detailed analysis; and at the second step, by applying time-consuming analysis on APs in the same human task, it can enhance the human task model. This human task model is highly abstracted and is able to change the degree of abstraction adapting to the environment so as to be applicable in a different environment. We describe this method and its implementation using data gloves and a stereo vision system. We also show an experimental result in which a real robot observed a human task and performed the same human task successfully in a different environment using this model.

Acquiring hand-action models by attention point analysis

This paper describes our current research on learning task level representations by a robot through observation of human demonstrations. We focus on human hand actions and represent such hand actions in symbolic task models. We propose a framework of such models by efficiently integrating multiple observations based on attention points; we then evaluate the model by using a human-form robot. We propose a two-step observation mechanism. At the first step, the system roughly observes the entire sequence of the human demonstration, builds a rough task model and extracts attention points (APs). The attention points indicate the time and position in the observation sequence that requires further detailed analysis. At the second step, the system closely examines the sequence around the APs and the obtained attribute values for the task model, such as what to grasp, which hand to be used, or what is the precise trajectory of the manipulated object. We implemented this system on a human form robot and demonstrated its effectiveness.