A key prerequisite to make user instruction of work tasks by interactive demonstration effective and convenient is situated multi-modal interaction aiming at an enhancement of robot learning beyond simple low-level skill acquisition. We report the status of the Bielefeld GRAVIS-robot system that combines visual attention and gestural instruction with an intelligent interface for speech recognit...

Researchers are becoming aware of the importance of other information sources besides visual data in robot learning by demonstration (LbD). Forcebased perceptions are shown to convey very relevant information – missed by visual and position sensors – for learning specific tasks. In this paper, we review some recent works using forces as input data in LbD and Human-Robot interaction (HRI) scenar...

Combining Fable robot, a modular robot, with a neuroinspired controller, we present the proof of principle of a system that can scale to several neurally controlled compliant modules. The motor control and learning of a robot module are carried out by a Unit Learning Machine (ULM) that embeds the Locally Weighted Projection Regression algorithm (LWPR) and a spiking cerebellar-like microcircuit....

We introduce a technique that allows a real robot to execute real-time learning, in which GP and RL are integrated. In our former research, we showed the result of an experiment with a real robot “AIBO” and proved the technique performed better than the traditional Q-learning method. Based on the proposed technique, we can acquire the common programs using a GP, applicable to various types of r...

We propose a method which acquires a purposive behavior for a mobile robot to shoot a ball into the goal by using a vision-based reinforcement learning. A mobile robot (an agent) does not need to know any parameters of the 3-D environment or its kinematics/dynamics. Information about the changes of the environment is only the image captured from a single TV camera mounted on the robot. An actio...

In this paper we describe a supervised robot learning method which enables a mobile robot to acquire the ability to follow walls and negotiate confined spaces by having these behaviours demonstrated with example actions. We achieve this by demonstrating the desired motion with a remote control while accumulating training data from the robot’s sensors and teacher’s instructions. To speed up lear...

In this paper 1 , the processes of exploration and of incremental learning in the robot navigation task are studied using the dynamical systems approach. A neural network model which performs the forward modeling, planning, consolidation learning and novelty rewarding is used for the robot experiments. Our experiments showed that the robot repeated a few variation of travel patterns in the begi...

We present an integrated framework that permits implicit task learning and kinesthetic teaching during the execution of robotic tasks in cooperation with humans. The proposed system combines physical human-robot interaction, attentional supervision, multimodal interaction to support robot teaching and incremental task learning. We describe the overall system architecture discussing a task learn...

The purpose of this paper is to propose a NeuralQ_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_lear...

The research of legged robot was rapidly developed. It can be seen from recent ideas about new systems of robot movement that take ideas from nature, called biology inspired. This type of robot begins replacing wheeled robot with various functions and interesting maneuvers ability. However, designers should decide how many legs are required to realize the ideas. One of the ideas that are rarely...