Decision Making in Robots and Animals
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Current artificial intelligence systems for perception and action incorporate a number of techniques: optimal observer models, Bayesian filtering, probabilistic mapping, trajectory planning, dynamic navigation, and feedback control. I will briefly describe and demonstrate some of these methods for autonomous driving and for legged and flying robots, and contrast these models with neural representations and computation. I will also highlight and discuss the role of noise and differences between synthetic and biological approaches to decision making.
- IRIM Seminar Series