Physical Scaffolding Accelerates the Evolution of Robot Behavior

Author:

Buckingham David1,Bongard Josh2

Affiliation:

1. Tufts University

2. University of Vermont

Abstract

In some evolutionary robotics experiments, evolved robots are transferred from simulation to reality, while sensor/motor data flows back from reality to improve the next transferral. We envision a generalization of this approach: a simulation-to-reality pipeline. In this pipeline, increasingly embodied agents flow up through a sequence of increasingly physically realistic simulators, while data flows back down to improve the next transferral between neighboring simulators; physical reality is the last link in this chain. As a first proof of concept, we introduce a two-link chain: A fast yet low-fidelity ( lo-fi) simulator hosts minimally embodied agents, which gradually evolve controllers and morphologies to colonize a slow yet high-fidelity ( hi-fi) simulator. The agents are thus physically scaffolded. We show here that, given the same computational budget, these physically scaffolded robots reach higher performance in the hi-fi simulator than do robots that only evolve in the hi-fi simulator, but only for a sufficiently difficult task. These results suggest that a simulation-to-reality pipeline may strike a good balance between accelerating evolution in simulation while anchoring the results in reality, free the investigator from having to prespecify the robot's morphology, and pave the way to scalable, automated, robot-generating systems.

Publisher

MIT Press - Journals

Subject

Artificial Intelligence,General Biochemistry, Genetics and Molecular Biology

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Guiding the Exploration of the Solution Space in Walking Robots Through Growth-Based Morphological Development;Proceedings of the Genetic and Evolutionary Computation Conference;2023-07-12

2. Engineering morphological development in a robotic bipedal walking problem: An empirical study;Neurocomputing;2023-03

3. A study of growth based morphological development in neural network controlled walkers;Neurocomputing;2022-08

4. Harnessing Growth-Based Morphological Development to Facilitate Learning ANN-Controlled Bipedal Walking;2022 International Joint Conference on Neural Networks (IJCNN);2022-07-18

5. Growth-based morphological development;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2022-07-09

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