Chapter 14. Active Vision for Goal-Oriented Humanoid Robot Walking (2/2) in Creating Brain-Like Intelligence, Sendhoff et al. Course: Robots Learning from.

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Chapter 14. Active Vision for Goal-Oriented Humanoid Robot Walking (2/2) in Creating Brain-Like Intelligence, Sendhoff et al. Course: Robots Learning from Humans Sangwoong Yoon Interdisciplinary Program in Neuroscience Biointelligence Laboratory Seoul National University

One-Page Summary Neural Architecture Input: Visual image Output: Robot action (vision & motor) Genetic Algorithm For parameter tuning Robot Path-Finding Task After many generations: The robot can reach the goal Active vision behavior is learned © 2015, SNU CSE Biointelligence Lab., 2

Experiment © 2015, SNU CSE Biointelligence Lab., 3 Robot SimulatorNeural Architecture w/ Active Vision

Experiment © 2015, SNU CSE Biointelligence Lab., 4 Generation 1 Generation 100 Use Genetic Algorithm to tune neural connections Goal: To reach the goal beacon (Illustrative examples) Goal Beacon Starting Point

Genetic Algorithm Optimization algorithm that mimics evolution © 2015, SNU CSE Biointelligence Lab., 5 Related words: Evolutionary computation, evolutionary programming, …

Discussion on Genetic Algorithm Good Sides SOUNDS FANCY Versatile Some times, GA is the only choice © 2015, SNU CSE Biointelligence Lab., 6 This Part Intentionally Left Blank

Discussion on Genetic Algorithm Bad Sides No theoretical guarantee So many hyperparameters And their meanings are not clear Many fitness evaluation needed © 2015, SNU CSE Biointelligence Lab., 7

Details of Experiment Fitness Function Numbers 1 Fitness = 4 Trial 147 neural weights 50 chromosomes Best 20% are selected and reproduced © 2015, SNU CSE Biointelligence Lab., 8

Result © 2015, SNU CSE Biointelligence Lab., 9 With active visionWithout active vision Max among pop Mean among pop Active vision component is crucial

Result © 2015, SNU CSE Biointelligence Lab., 10 ◀ Best robot’s trajectory ▼Best robot’s camera movement Robot looks for obstacles (?!)

Paper’s Conclusion We’ve built a self-evolving active vision system (Yay!) Active vision is helpful (Yay!) GA is a bad choice Not scalable, extremely inefficient Backpropagation / Reinforcement Learning Will it work in the different map? © 2015, SNU CSE Biointelligence Lab., 11 My Discussion

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