In this paper we describe a novel robot learning method that enables a mobile robot equipped with sonar and IR light sensors to automatically acquire the ability to negotiate objects and dock by simply interacting with the environment. We achieve this by providing the robot with sonar and IR sensors for detecting objects and the relative direction of IR beacons placed in the environment. A set ...

in this project guidance and control of underwater robot, including three engines and propeller attached to it, by fuzzy control has been investigated. fuzzy control is done based on human experience and requiered laws. the robot can also be controlled and guided by the user. this robot may be used in sea or swimming pool environment for finding goal point and locate in desired direction. in ad...

Minimalist neural mechanisms are suitable tools for programming and training autonomous robots. This paper explores the limitations of hand-crafted minimalist robot control mechanisms based on a neural paradigm, and then shows that these mechanisms are well suited to robot training using well understood neural learning mechanisms. Training a robot is more powerful than other methods more common...

We present a neural network that learns to control approach and avoidance behaviors in a mobile robot based on three forms of animal learning: classical conditioning, operant conditioning, and habituation. Mechanisms of classical conditioning are used to learn to predict the proximity of obstacles and sources of light. At the same time the robot learns through operant conditioning to generate t...

In this paper, a new method was proposed for the navigation of a mobile robot in an unknown dynamic environment. The robot could detect only a limited radius of its surrounding with its sensors and it went on the shortest null space (SNS) toward the goal. In the case of no obstacle, SNS was a direct path from the robot to goal; however, in the presence of obstacles, SNS was a space around the r...

We explore the use of a connectionist-learning system designed to allow the application of reinforcement learning to robot control. In particular, we compare direct and indexed partitioning methods and nd indexed partitioning has advantages in time and space complexity, learning speed (measured in trials), and success rate. We make these comparisons based on extensive simulations and runs on a ...

Robot learning in simulation is a promising alternative to the prohibitive sample cost of learning in the physical world. Unfortunately, policies learned in simulation often perform worse than hand-coded policies when applied on the physical robot. This paper proposes a new algorithm for learning in simulation – Grounded Action Transformation – and applies it to learning of humanoid bipedal loc...

Deep learning has allowed a paradigm shift in pattern recognition, from using hand-crafted features together with statistical classifiers to using general-purpose learning procedures for learning data-driven representations, features, and classifiers together. The application of this new paradigm has been particularly successful in computer vision, in which the development of deep learning meth...

Imitative learning and learning by observation are social mechanisms that allow a robot to acquire knowledge from a human or another robot. However to be able to obtain skills in this way the robot faces many complex issues, one of which is that of finding solutions to the correspondence problem. Evolutionary predecessors to observational imitation may have been self-imitation where an agent av...

A neural architecture that makes possible the integration of a kinematic adaptive neuro-controller for trajectory tracking and an obstacle avoidance adaptive neuro-controller is proposed for nonholonomic mobile robots. The kinematic adaptive neuro-controller is a real-time, unsupervised neural network that learns to control a nonholonomic mobile robot in a nonstationary environment, which is te...