1. Ch 14. Active Vision for Goal-Oriented Humanoid Robot Walking (1/2) Creating Brain-Like Intelligence, Sendhoff et al. (eds), Robots Learning from Humans, Fall 2015
Summarized by Jin-Hwa Kim
Biointelligence Laboratory
Program in Cognitive Science
Seoul National University

2. Contents 14.1 Introduction 14.2 Robotic Setup and Neural Architecture
14.3 Evolution of Neural Controllers of Hoap-2 Humanoid Robot
14.4 Discussion
14.5 Conclusion

3. © 2015, SNU Biointelligence Lab., http://bi.snu.ac.kr
Overview of Chapter 14
Complex visual tasks may be performed by a co-evolutionary process of active vision and feature selection.
To validate this hypothesis more further, a goal-oriented bipedal humanoid is used:
A primitive vision system on its head is evolved while exploring
Tolerate visual perturbation owing to own walking dynamics
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4. Ch 14. Active Vision for Goal-Oriented Humanoid Robot Walking
4.1 Introduction
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5. © 2015, SNU Biointelligence Lab., http://bi.snu.ac.kr
Terminology
Active Vision
A sequential and interactive process of selecting and analyzing parts of a visual scene.
It reduces a computational cost using two-step process, permitting a heuristic search of a partial area on an entire image in the first step.
Feature Selection
Sensitivity to relevant features to which the system selectively responds
Task-aware selection of partial information
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6. © 2015, SNU Biointelligence Lab., http://bi.snu.ac.kr
Neural Architecture
A) visual neurons with non-overlapping receptive fields whose inputs are grey levels of the corresponding pixels in a given image B) C) proprioceptive information about the movement of the vision system D) a set of outputs determining the behavior of the system (performing tasks) E) a set of outputs determining the behavior of the vision system (active vision) F) a set of evolvable synaptic connections
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7. © 2015, SNU Biointelligence Lab., http://bi.snu.ac.kr
Neural Architecture
Selecting features in A & F to perform a given task (D), at the same time, control the vision system (E).
The synaptic strengths of the network (F) were encoded in a binary string and evolved with a genetic algorithm while freely exploring.
Size and position invariant shape discrimination.
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8. 4.2 Robotic Setup and Neural Architecture
Ch 14. Active Vision for Goal-Oriented Humanoid Robot Walking
4.2 Robotic Setup and Neural Architecture
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9. © 2015, SNU Biointelligence Lab., http://bi.snu.ac.kr
Robotic Setup
Humanoid robot Hoap-2
25cm(W) x 16cm(L) x 50cm(H)
Simulated by Webots™
Goal
To reach a designated location by detecting the beacon (white window) while avoiding obstacles (black cylinders) and walls.
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10. Extended Neural Architecture
A set of proprioceptive neurons provides information about the movement of the head camera with respect to the upper torso of the robot. (pan & tilt angles)
Memory units are copies of previous outputs recurrently giving more dynamics.
Bias provides adaptive thresholds of output neurons.
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11. Extended Neural Architecture
Zoom: zooming factor
Filter: visual neurons filtering
Pan & tilt: new velocities of the camera
Dir & speed: walking direction and speed of the robot
Outputs use the sigmoid activation function f(x) = 1/(1+exp(-x)), where x is a weighted sum of all inputs.
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12. © 2015, SNU Biointelligence Lab., http://bi.snu.ac.kr
Summary
Macroscopic Control
The algorithm of bipedal walking itself is beyond our research scope.
To control macroscopic behavior, visuo-motor coordination exploiting active vision and feature selection is used.
In next two chapters
We will discuss evolution of neural controllers of Hoap-2 Humanoid robot.
© 2015, SNU Biointelligence Lab.,