Robot Brain Project CREST Development of Brain-Informatics Machines through Dynamical Connection of Autonomous Motion Primitives
Robot Brain Project
Nakamura Group
Asada Group
Tsuchiya Group
Ushio Group
Yoshizawa Group
Sasaki Group

Results | Asada Group

Information Processing for Learning and Acquisition of Behaviors
Minoru Asada*1*2
*1Osaka Univ.,*2Handa FRC

Our research group at Graduate School of Engineering, Osaka University, has been seeking for the methods of behavior learning to accomplish the given task. The approach is not task-specific but expectant of being meaningful from a viewpoint of information processing in our brain, and we have been attacking several kinds of issues by constructing a model and verifying it through real robot experiments. Summary of the following three issues are given:

1. observation strategy learning for decision making of small quadruped based on information theory:The aim of this research is to propose an efficient observation strategy for action decision of a small quadruped robot. We define the efficiency by the time used for observation to make a decision. We compare the contribution of the observation by the information gain. The observation strategy we propose is to do observations in the order of the information gain. First, we proposed a method which requires a robot to stand still to observe and make a decision. Then, we proposed an extension which enables observation during walking motion.
2. multi-layered learning systems for vision-based behavior acquisition of a real mobile robot:We proposed a mechanism which constructs learning modules at higher layers using a number of groups of modules at lower layers. The modules in the lower networks are self-organized as experts to move into different categories of sensor value regions and learn lower level behaviors using motor commands. In the meantime, the modules in the higher networks are organized as experts which learn higher level behavior using lower modules. We applied the method to a simple soccer situation in the context of RoboCup, and showed the the validity of this method.

3. vision-based reinforcement learning for humanoid behavior generation with rhythmic walking parameters:A method for generating vision-based humanoid behaviors by reinforcement learning with rhythmic walking parameters is given. A rhythmic motion controller such as CPG or neural oscillator stabilizes the walking. The learning process consists of building an action space with two parameters (a forward step width and a turning angle) so that infeasible combinations are inhibited, and reinforcement learning with the constructed action space and the state space consisting of visual features and posture parameters to find a feasible action. The method is applied to a situation from the Humanoid RoboCupSoccer league in RoboCup, that is, to approach the ball and to shoot it into the goal. Instructions by human are given to start up the learning process, and the rest is solely self-learning in real situations.

Asada Group
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