In this paper, two mode Q-learning, an extension of Q-learning, is used to stabilize the Zero Moment Point (ZMP) of a biped robot in the standing posture. In two mode Q-learning, the experiences of both success and failure of an agent are used for fast convergence. To demonstrate the effectiveness of two mode Q-learning against conventional Q-learning, the property of convergence is investigate...

In multi-robot domain, one of the important problems is to achieve cooperation among robots. In this paper we propose a hybrid system that integrates reinforcement learning and flocking control in order to create adaptive and intelligent multi-robot systems. We study two problems of multi-robot concurrent learning of cooperative behaviors: (1) how to generate efficient combination of high level...

We have applied Q-learning method so that a mobile robot acquire a shooting behavior of a soccer game. Thought the acquired behavior is tested in RoboCup-97, the performance is limited because of the narrow visual angle of the robot. Therefore, we install an omnidirectional vision into a mobile robot to enlarge a visual angle and apply Q-learning to behavior acquisition of the robot. An attenti...

Editor’s key points † A novel robot assistance nerve block system has been recently developed and tested in patients. † This study investigated the role of this system in training in ultrasound-guided nerve blocks. † The robot system reduced inter-subject variability, with faster learning of needle placement than manual techniques. † This new approach to regional anaesthesia training may increa...

The problem of learning a policy, a task representation mapping from world states to actions, lies at the heart of many robotic applications. One approach to acquiring a task policy is learning from demonstration, an interactive technique in which a robot learns a policy based on example state to action mappings provided by a human teacher. This thesis introduces Confidence-Based Autonomy, a mi...

As human-robot collaboration methodologies develop robots need to adapt fast learning methods in domestic scenarios. The paper presents a novel approach to learn associations between the human hand gestures and the robot’s manipulation actions. The role of the robot is to operate as an assistant to the user. In this context we propose a supervised learning framework to explore the gesture-actio...

We address the problem of imitation learning in interactive robots which learn from task demonstrations. Many current approaches to interactive robot learning are performed over a set of demonstrations, where the robot observes several demonstrations of the same task and then creates a generalized model. In contrast, we aim to enable a robot to learn from individual demonstrations, each of whic...

This paper proposed a robot reinforcement learning method based on learning classifier system. A Learning Classifier System is a accuracy-based machine learning system with gradient descent that combines reinforcement learning and rule discovery system. The genetic algorithm and the covering operator act as innovation discovery components which are responsible for discovering new better reinfor...

Reinforcement learning methods are useful for robot learning, but become slow when robots possess many degrees of freedom. We suggest equipping robots with fast on-board simulators, in order to accelerate learning. Such simulators will resemble forms of consciousness, enabling the robots to perform run-time trials in a simulated world, rather than tediously performing them in practice. We have ...

This paper presents collaboration of behavior based control and fuzzy Q-learning for mobile robot navigation systems. There are many fuzzy Qlearning algorithms that have been proposed to yield individual behavior like obstacle avoidance, find target and so on. However, for complicated tasks, it is needed to combine all behaviors in one control schema using behavior based control. Based this fac...