Learning to control self-assembling morphologies: A study of generalization via modularity
Name
NeurIPS-2019-learning-to-control-self-assembling-morphologies-a-study-of-generalization-via-modularity-Paper.pdf
Description
Published version
Size
3.78 MB
Format
Adobe PDF
Checksum (MD5)
2fd19bda42a299edac39ddf778dbaa37
Author(s)
Isola, Phillip John
Date Issued
December 2019
Journal
Advances in Neural Information Processing Systems
Citation
Pathak, Deepak et al. “Learning to control self-assembling morphologies: A study of generalization via modularity.” Advances in Neural Information Processing Systems, 32 (December 2019) © 2019 The Author(s)
Version
Final published version
Abstract
Contemporary sensorimotor learning approaches typically start with an existing complex agent (e.g., a robotic arm), which they learn to control. In contrast, this paper investigates a modular co-evolution strategy: a collection of primitive agents learns to dynamically self-assemble into composite bodies while also learning to coordinate their behavior to control these bodies. Each primitive agent consists of a limb with a motor attached at one end. Limbs may choose to link up to form collectives. When a limb initiates a link-up action, and there is another limb nearby, the latter is magnetically connected to the 'parent' limb's motor. This forms a new single agent, which may further link with other agents. In this way, complex morphologies can emerge, controlled by a policy whose architecture is in explicit correspondence with the morphology. We evaluate the performance of these dynamic and modular agents in simulated environments. We demonstrate better generalization to test-time changes both in the environment, as well as in the structure of the agent, compared to static and monolithic baselines. Project video and code are available at https://pathak22.github.io/modular-assemblies/.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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