Robot End Effector Tracking Using Predictive Multisensory Integration

Robot End Effector Tracking Using Predictive Multisensory Integration

We propose a biologically inspired model that enables a humanoid robot to learn how to track its end effector by integrating visual and proprioceptive cues as it interacts with the environment. A key novel feature of this model is the incorporation of sensorimotor prediction, where the robot predict...

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Bibliographic Details
Main Authors: Lakshitha P. Wijesinghe, Jochen Triesch, Bertram E. Shi
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-10-01
Series:Frontiers in Neurorobotics
Subjects:
active efficient coding
developmental robotics
sensorimotor prediction
generative adaptive subspace self-organizing map
reinforcement learning
Online Access:https://www.frontiersin.org/article/10.3389/fnbot.2018.00066/full

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https://www.frontiersin.org/article/10.3389/fnbot.2018.00066/full

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