A neural network-based exploratory learning and motor planning system for co-robots

Abstract

Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or “learning by doing,” an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

PSO-based Minimum-time Motion Planning for Multiple Vehicles Under Acceleration and Velocity Limitations

“…Some approaches using control design were revealed in [14][15][16][17][18][19][20][21][21][22][23][24][25][26][27]. The problem of point-to-point control has been addressed in various works: The navigation control approaches proposed in [14][15][16][17][18][19][20][21][22][23][24][25][26][27] rigorously guaranteed that the vehicles would reach their final configurations. To deal with the changes in vehicles' internal parameters, some adaptive control designs [18][19][20][21][22] as well as adaptive control/neural network combinations were introduced [23][24][25][26].…”

A Developmental Learning Approach of Mobile Manipulator via Playing

“…Intelligent robots have been widely applied to support or even replace the work of humans in many social activities, such as assembly lines, family services, and social entertainment. These robots are made intelligent by many methods proposed in the literature, with the most common ones being mathematical modeling and dynamics models, such as Yan et al ( 2013 ), Galbraith et al ( 2015 ) and Grinke et al ( 2015 ). These methods utilize predefined cognitive architectures in the intelligent systems, which cannot be used for significant changes during the interaction within the environment.…”

Neural Plasticity for Rich and Uncertain Robotic Information Streams

“…Uncertainty and large amount of data are also found in collaborative multi-robot scenarios in which multiple robots work alongside humans. Galbraith and colleagues propose a motor babbling approach to learn a complex set of relations and interactions with the 11-degrees-of-freedom RoPro Calliope mobile robot (Galbraith et al, 2015). Motor babbling of its wheels and arm enabled the Calliope to learn how to relate visual and proprioceptive information to achieve hand-eye-body coordination.…”