Add to ORKGThis paper presents a neural controller that learns goal-oriented obstacle-avoiding reaction strategies for a multilink robot arm. It acquires these strategies on-line from local sensory data.Results for a two-link robot arm show that the combination of both modules speeds up the learning process.JRC.(ISIS)-Institute For Systems, Informatics And Safet
This paper reports on a navigation system for robotic manipulators. The control system combines a re...
This paper deals with the reactive control of an autonomous robot which move safely in a crowded rea...
Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the ...
Abstract — In this work we present a reinforcement learning system for autonomous reaching and grasp...
In the process of human learning, the brain which acts as a controller receive sensory signals from ...
By beginning with simple reactive behaviors and gradually building up to more memory-dependent behav...
Existing approaches for learning to control a robot arm rely on supervised methods where correct beh...
In the past few years, the field of autonomous robot has been rigorously studied and non-industrial ...
This paper describes work in progress on a neural-based reinforcement learning architecture for the ...
This paper describes a reinforcement connec-tionist learning mechanism that allows a goal-directed a...
We present a new reinforcement learning system more suitable to be used in robotics than existing on...
. Recently it has been introduced a neural controller for a mobile robot that learns both forward an...
This paper des ribes a neural network-based ar hite ture for reinfor ement learning of robot ontrol ...
A neural network controller is proposed for the motion control of robot manipulators with force/torq...
This thesis develops a novel approach to robot control that learns to account for a robot's dynamic ...
This paper reports on a navigation system for robotic manipulators. The control system combines a re...
This paper deals with the reactive control of an autonomous robot which move safely in a crowded rea...
Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the ...
Abstract — In this work we present a reinforcement learning system for autonomous reaching and grasp...
In the process of human learning, the brain which acts as a controller receive sensory signals from ...
By beginning with simple reactive behaviors and gradually building up to more memory-dependent behav...
Existing approaches for learning to control a robot arm rely on supervised methods where correct beh...
In the past few years, the field of autonomous robot has been rigorously studied and non-industrial ...
This paper describes work in progress on a neural-based reinforcement learning architecture for the ...
This paper describes a reinforcement connec-tionist learning mechanism that allows a goal-directed a...
We present a new reinforcement learning system more suitable to be used in robotics than existing on...
. Recently it has been introduced a neural controller for a mobile robot that learns both forward an...
This paper des ribes a neural network-based ar hite ture for reinfor ement learning of robot ontrol ...
A neural network controller is proposed for the motion control of robot manipulators with force/torq...
This thesis develops a novel approach to robot control that learns to account for a robot's dynamic ...
This paper reports on a navigation system for robotic manipulators. The control system combines a re...
This paper deals with the reactive control of an autonomous robot which move safely in a crowded rea...
Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the ...