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

Byron Galbraith ,Frank H Guenther,Massimiliano Versace

doi:10.3389/fnbot.2015.00007

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.

Highlights

Co-robots, collaborative robots that work alongside humans to perform assistive tasks, are becoming more prevalent, notably in the healthcare and telepresence spaces (Kristoffersson et al, 2013)
The hand-eye-body coordination tasks were evaluated in three broad task areas: hand-eye coordination, egocentric navigation, and grasping distant objects (Figure 7)
We presented a control system with an eye toward co-robots that used motor babbling to enable a robot to learn about aspects of its own configuration in regards to hand-eye-body coordination

Summary

Introduction

Co-robots, collaborative robots that work alongside humans to perform assistive tasks, are becoming more prevalent, notably in the healthcare and telepresence spaces (Kristoffersson et al, 2013). A major challenge for co-robots is the need to make decisions on how to operate in dynamic environments with other autonomous agents (Hayes and Scassellati, 2013). This includes using onboard sensors to detect and avoid obstacles or finding, reaching for, and grasping objects. The sensory processing, decision-making, and motor planning components of the task process all share this reference frame in order to achieve effective coordination. The co-robot needs to know where its body and arm are relative to a visually identified target object in order to plan and execute the appropriate motor actions needed to achieve its goal of grasping

Methods

Results

Discussion

Conclusion