The proposal for deciding effective action using prediction of internal robot state based on internal state and action

For a robot working in a complicated environment, it is virtually impossible to predict all possible situations and to pre-program the robot with all suitable reaction patterns for each of the possible situations. Because robots are required to act differently in different situations. Furthermore, robots should be able to adapt to different environments by deciding upon the course of action to take depending on the situation, in addition to pre-registered commands, in a manner similar to humans. However, hardware and the limited computational resources pose a physical limitation, so the robot needs some time to decide its course of action. In this context, if robots will be able to select the most appropriate action quickly and can cancel the time delay caused by the limitations mentioned above. Moreover, if depending on the action a robot takes, the future internal state space will vary infinitely. If we take this point into consideration, we need to simultaneously predict the internal state and the action that the robot adopt. The purpose of this research is to compensate the current action as the appropriate action using next time and future actions that robots will take. For achievement of this, first, we state advance prediction using Online SVR as a learner. This Online SVR predicts the future internal robot state - i.e., the robot´s next internal state to be taken. Furthermore, this predictor will be useful for predicting the distant future internal robot state, using the internal state that the robot adopt repeatedly. Second, we determine the future action from Optimal Feedback Controller using predicted future internal state - i.e., the robot´s next action to be taken. In this paper, we designed a controller using LQR (Linear Quadratic Regulator) and use as determine an action. This paper presents the results of these studies and discusses methods that allow the robot to decide its desirable behavior quickly, using the state predicted. As an application - xample, we used two-wheeled inverted pendulum, and compared the results of the proposed method with the actual response of the inverted postural control task.