1. 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

2. 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., http://bi.snu.ac.kr 2

3. Experiment © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 3 Robot SimulatorNeural Architecture w/ Active Vision

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

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

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

7. 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., http://bi.snu.ac.kr 7

8. 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., http://bi.snu.ac.kr 8

9. Result © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 9 With active visionWithout active vision Max among pop Mean among pop Active vision component is crucial

10. Result © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 10 ◀ Best robot’s trajectory ▼Best robot’s camera movement Robot looks for obstacles (?!)

11. 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., http://bi.snu.ac.kr 11 My Discussion