Tomoaki Yoshikai

Building Spined Muscle-Tendon Humanoid

Human can perform variety of limber whole-body motions using numerous muscles and huge number of various sensors. The human brain has all the connections to the sensors and muscles, and learn how to manage them for whole-body motions. In this research, we have aimed to build a complex body with physically massive parallel sensor-motor systems to enter the next stage for studying humanoid brain systems. It is designed to have a flexible spined torso and a whole-body with fully muscle-tendon driven systems. In this paper the design and implementation of the first model of the humanoid is described with some experiments.

Design and implementation of reinforceable muscle humanoid

We propose a concept of reinforceable-muscle humanoid with hyper parallel muscle-tendon systems. By the facilitation of changing the assignment of actuators, we can easily strengthen a specific part of robot body, while changing the arrangement of actuators of current humanoid robots is quite difficult. By developing muscle units in each of which a motor and sensors are integrated, and by adopting musculo-skeletal structure, the rearrangeability of muscles has been realized. This paper describes the concept, prototype design and implementation of the reinforceable muscle humanoid, and the evaluating experiment by a musculo-skeletal humanoid is presented.

Multi-sensor guided behaviors in whole body tendon-driven humanoid Kenta

In generating sensor-based behavior of the robot, which has various sensors and many degrees of freedom, the key point is how it utilizes information from many sensors in multi modals for moving its body flexibly. In our experimental systems for generating multi-sensor guided behavior, we have introduced the following methods: 1) Integration with reflexes: Integrating objective behavior (behavior which is aimed for achieving some specific goals) and various kinds of reflexes (behavior which instantly reacts to the changes in an environment).; 2) Posture-sensor data memory: Memorizing the robot postures, relating them with changes of various sensors. This memory can also be used for predicting human intention from previous experiences.; and 3) ABC-Net (attention-based conditional network): Behavior description network, where nodes are expressed by states of sensors paid attention to and arcs are expressed by actions to transit between nodes. In those methods, behavior experiments for making sure the effectiveness have been done in both real and virtual environment using Kenta, a multi-DOF and multi-sensor humanoid that we have developed. In this paper, the design and implementation of the whole behavior systems including the above three methods for realizing the multi-sensor guided behavior are described and the results of the behavior experiments using Kenta are shown.