Dynamic intelligence through online optimization

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The objective of this research is to develop algorithms and software for control of complex robotic devices. The approach is to combine online re-planning and offline learning within a novel mathematical framework, yielding richer and more adaptive behavior than what is currently possible.Intellectual MeritOnline trajectory optimization is the method of choice for controlling slow and smooth dynamics such as chemical processes. However robot dynamics are much faster and non-smooth, presenting formidable challenges to existing methods. The proposed work will overcome these challenges, by leveraging a new mathematical framework for stochastic optimal control where the problem is reduced to a linear equation even though the underlying dynamics are nonlinear. These algorithms will use a new physics engine that relies on parallel computing to simulate robot dynamics orders-of-magnitude faster than real-time. Broader ImpactThe proposed work will change how robots and animated characters move. Currently many robotic systems are controlled in open loop, or are designed to execute one specific task well but cannot be versatile. Animation is mostly hand-drawn or based on playback of motion capture data. This work will enable both robots and animated figures to express more natural and versatile movements. Another important contribution is to neuroscience and biomechanics, where many researchers believe that the brain controls the body optimally, yet it is difficult to predict what the optimal movements are in complex tasks. The algorithms developed here will generate such predictions, and enable quantitative model-data comparisons advancing our understanding of sensorimotor brain function.