Tsutomu Kunitachi

Motion control for humanoid robots based on the motion phase decision tree learning

Humanoid robots, due to their link structure with high degree of freedom and the substitutability for human work, require a sophisticated motion control technique regardless of the type of motions or the environments. This paper gives a concept learning-based approach to this problem. We propose a motion generation method based on decision tree learning with motion phase. The system can generate a stable and anti-tumble motion which transforms the robot into a target posture. In experiment, the target motion are to stand up from a chair. Some stable and anti-tumble motions to stand up from a chair were performed by humanoid robot HOAP-1. In this paper, we discuss the validity of motion control considering motion phase.

A rough set approach to extract painting composition rules

Non-photorealistic rendering (NPR) is a technique used in the field of computer graphics. Here, we restrict our attention to the subset of NPR developed to synthesize drawing and painting techniques. Specifically, we focus on the style of Cubist art, such as the works of Picasso. First, we examine the actual works of Picasso in order to understand and improve the qualities of Cubist style rendering. As a result, the painting composition rules are expected to be extracted from the analysis of Picassos works. In the present paper, we propose a Cubist style rendering based on the analysis of painting compositions. The present paper describes the proposed method by which to construct a painting composition database and illustrates the rules extracted from the database. We use rough set theory to extract the rules, and using these rules; we render and illustrate a sample image in Cubist style.