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Latest News for March 9th, 2018

Efficient Model-Based and Data-Driven Methods to Learn from Small Data in Robot Planning and Control

Robot motion planning and control in real-world settings is hindered, in part, by uncertainty. Dealing with uncertainty is a difficult problem because it invalidates the performance guarantees often available in deterministic cases, while its precise effect on motion cannot be predicted. Further, (autonomous) robot performance often emerges through the interaction of multiple components, mainly including...