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- W2989969123 abstract "Developing efficient walking gaits for biomechanical robots is a difficult task that requires optimizing parameters in a continuous, multidimensional space. In this paper we present a new framework for learning complex gaits with musculoskeletal models. We use Deep Deterministic Policy Gradient which is driven by the external control command, and apply curriculum learning to acquire a reasonable starting policy. We accelerate the learning process with large-scale distributed training and bootstrapped deep exploration paradigm. As a result, our approach won the NeurIPS 2018: AI for Prosthetics competition, scoring more than 30 points than the second placed solution." @default.
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- W2989969123 date "2019-11-30" @default.
- W2989969123 modified "2023-09-24" @default.
- W2989969123 title "Efficient and Robust Learning on Elaborated Gaits with Curriculum Learning" @default.
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- W2989969123 doi "https://doi.org/10.1007/978-3-030-29135-8_10" @default.
- W2989969123 hasPublicationYear "2019" @default.
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