This lecture covers the concepts of feedback and adaptation in visual intelligence, including model-based reinforcement learning, model predictive control, inner and outer loop feedback mechanisms, rapid motor adaptation for legged robots, and network adaptation methods. The instructor discusses how these techniques enable machines to improve their performance in dynamic environments by adjusting their behavior based on feedback signals and predictive models.
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