On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills

Understand

Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories.

• This is likely due to practical limitations, such as significant computational burden, inscrutable learned behaviors, sensitivity to initialization, and the considerable technical expertise required for implementation.
• In this work, we investigate the utility of Koopman operator theory in alleviating these limitations.
• Koopman operators are simple yet powerful control-theoretic structures to represent complex nonlinear dynamics as linear systems in higher dimensions.