Shared Modular Recurrence in Contextual MDPs for Universal Morphology Control
Overview
This paper introduces a universal controller architecture leveraging shared modular recurrence within contextual Markov decision processes to enhance data and computational efficiency. The authors address the challenge of controlling robots with highly dissimilar morphologies, particularly when generalizing to unseen agents where contextual features are partially available. By recovering these features through modular interactions, the proposed transformer-based approach demonstrates substantial improvements in zero-shot generalization performance across four different environments using a large set of MuJoCo robots with varying dynamics, kinematics, and topologies.
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- arXiv Robotics — research abstractsSignalShared Modular Recurrence in Contextual MDPs for Universal Morphology Control
This paper introduces a universal controller architecture leveraging shared modular recurrence within contextual Markov decision processes to enhance data and computational efficiency. The authors address the challenge of controlling robots with highly dissimilar morphologies, particularly when generalizing to unseen agents where contextual features are partially available. By recovering these features through modular interactions, the proposed transformer-based approach demonstrates substantial improvements in zero-shot generalization performance across four different environments using a large set of MuJoCo robots with varying dynamics, kinematics, and topologies.
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