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Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
Deepak Pathak · Christopher Lu · Trevor Darrell · Phillip Isola · Alexei Efros

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #197

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/

Author Information

Deepak Pathak (UC Berkeley, FAIR, CMU)
Christopher Lu (UC Berkeley and Covariant.ai)
Trevor Darrell (UC Berkeley)
Phillip Isola (Massachusetts Institute of Technology)
Alexei Efros (UC Berkeley)

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