Abstract
Bipedal walking is a complex whole-body motion with inherently unstable dynamics that makes the design of a robust controller particularly challenging. While a walk controller could potentially be learned with the hardware in the loop, the destructive nature of exploratory motions and the impracticality of a high number of required repetitions render most of the existing machine learning methods unsuitable for an online learning setting with real hardware. In a project in the DFG Priority Programme Autonomous Learning, we are investigating ways of bootstrapping the learning process with basic walking skills and enabling a humanoid robot to autonomously learn how to control its balance during walking.
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This work has been supported by grant BE 2556/6-1 of the German Research Foundation (DFG).
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Missura, M., Behnke, S. Online Learning of Bipedal Walking Stabilization. Künstl Intell 29, 401–405 (2015). https://doi.org/10.1007/s13218-015-0387-7
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DOI: https://doi.org/10.1007/s13218-015-0387-7