Tradeoffs in Neuroevolutionary Learning-Based Real-Time Robotic Task Design in the Imprecise Computation Framework (2019)
Pei-Chi Huang, Luis Sentis, Joel Lehman, Chien-Liang Fok, Aloysius K. Mok, Risto Miikkulainen
A cyberphysical avatar is a semi-autonomous robot that adjusts to an unstructured environment and performs physical tasks subject to critical timing constraints while under human supervision. This article first realizes a cyberphysical avatar that integrates three key technologies: body-compliant control, neuroevolution, and real-time constraints. Body-compliant control is essential for operator safety, because avatars perform cooperative tasks in close proximity to humans; neuroevolution (NEAT) enables “programming” avatars such that they can be used by non-experts for a large array of tasks, some unforeseen, in an unstructured environment; and real-time constraints are indispensable to provide predictable, bounded-time response in human-avatar interaction. Then, we present a study on the tradeoffs between three design parameters for robotic task systems that must incorporate at least three dimensions: (1) the amount of training effort for robot to perform the task, (2) the time available to complete the task when the command is given, and (3) the quality of the result of the performed task. A tradeoff study in this design space by using the imprecise computation as a framework is to perform a common robotic task, specifically, grasping of unknown objects. The results were validated with a real robot and contribute to the development of a systematic approach for designing robotic task systems that must function in environments like flexible manufacturing systems of the future.
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ACM Transactions on Cyber-Physical Systems, Vol. 3 (2019). DOI 0.1145/3178903.
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Joel Lehman Postdoctoral Alumni joel [at] cs utexas edu
Risto Miikkulainen Faculty risto [at] cs utexas edu
NEAT C++ The NEAT package contains source code implementing the NeuroEvolution of Augmenting Topologies method. The source code i... 2010