The goal of the proposed research is to provide efficient methods for defining, selecting and encoding multi-modal control programs. To this end, modes are recovered from system observations, i.e. quantized input-output strings are converted into consistent mode sequences within the Motion Description Language (MDL) framework. The design of such modes can help identify and predict the behaviors of complex systems (e.g. biological systems such as insects) and inspire the design and control of robust semi-autonomous systems (e.g. navigating robots).In this work, the efficiency of a method will be defined by the complexity and expressiveness of specific control programs. The insistence on low-complexity programs is originally motivated by communication constraints on the computer control of semi-autonomous systems, but also by our belief that, as complex as they may look, natural systems indeed use short motion schemes with few basic behaviors. The attention is first focused on the design of such short-length, few-distinct-modes mode sequences within the MDL framework. Optimal control problems are then addressed. In particular, given a mode sequence, the question of deciding when the system should switch from one mode to another in order to achieve some reachability requirements is studied. Finally, we propose to investigate how sampling strategies affect complexity and reachability, and how an acceptable trade-off between these conflicting entities can be reached.
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Multi-Modal Control: From Motion Description Languages to Optimal Control
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