1. Robot Intelligence Technology Lab. Evolutionary Robotics Chapter 3. How to Evolve Robots 2007. 1. 12 Chi-Ho Lee

2. 2 Robot Intelligence Technology Lab. 3.1 Introduction Issues to evolve robots Mechanical robustness  High speed collisions against objects  Sustained strong currents to motors pushing toward a wall Energy supply  Evolutionary experiments last longer time Analysis  The control system of evolved robots can be very complex Time efficiency  In some circumstances, time can be considerably reduced. Design of fitness function  The selection criterion can have a major influence on the results of an evolutionary run.

3. 3 Robot Intelligence Technology Lab. 3.2 Evolution of physical robots Khepera Two lateral wheels 8 active infrared sensors – 6 on one side and 2 on the other Modular open architecture – expanded with additional components

4. 4 Robot Intelligence Technology Lab. 3.2 Evolution of physical robots Khepera Modular open architecture – expanded with additional components  Vision turret K213  Gripper module

5. 5 Robot Intelligence Technology Lab. 3.2 Evolution of physical robots Khepera A device emitting laser beams – to measure behavior of a Khepera robot Its position and sensor readings are sent every 300 ms The evolved robot is not “aware” of these observations

6. 6 Robot Intelligence Technology Lab. 3.2 Evolution of physical robots Koala A larger robot fully compatible with the Khepera 16 infrared sensors with scaled-up measurement range A modular robot with parallel communication bus Used at investigating cross platform incremental evolution

7. 7 Robot Intelligence Technology Lab. 3.2 Evolution of physical robots An evolutionary experiment Connected to the computer through a serial cable The evolutionary algorithm and the neural controllers run on the computer Every 100ms, the evolving controller on the computer reads in the sensory activations from the robot An evolutionary experiment may take a few hours or days without any human intervention. Repositioning strategy was used

8. 8 Robot Intelligence Technology Lab. 3.3 Evolving in simulation Simulation To avoid the problem of time Problems claimed by Brooks (1992)  “Without regular validation on real robots, there is a great danger that much effort will go into solving problems that simply do not come up in the real world”  “There is a real danger that programs which work well on simulated robots will completely fail on real robots because of the differences in real world sensing and actuation” Several evolutionary experiments recently validated on real robots. Simplifies aspects such as energy supply and resetting the state of the environment at the beginning of each epoch. 2 techniques  To model an accurately as possible  To model only relevant characteristics

9. 9 Robot Intelligence Technology Lab. 3.3 Evolving in simulation Methods for accurately modeling robot-environment interactions 1.Due to the fact that different physical sensors and actuators, even if apparently identical may perform differently.

10. 10 Robot Intelligence Technology Lab. 3.3 Evolving in simulation 1.Due to the fact that different physical sensors and actuators, even if apparently identical may perform differently. (continued)  Its application to complex environment can become expensive  Can rely on mathematical models : modeled the wheel speeds, the infrared, and the ambient light sensors of the robot with a set of general equations In the case of a cylindrical object In the case of a rectalgular box In the case of two object

11. 11 Robot Intelligence Technology Lab. 3.3 Evolving in simulation Methods for accurately modeling robot-environment interactions 2.Physical sensors deliver uncertain values and commands to actuators have uncertain effects.  Introducing noise in simulation at all levels (it must be added in the appropriate amount) 3.The body of the robot and the characteristics of the environment should be accurately reproduced in the simulation  Grid worlds are meaningless for the purpose of developing robot controllers because they are inaccurate

12. 12 Robot Intelligence Technology Lab. 3.3 Evolving in simulation Minimal simulations To model only base set features, that are relevant for the emergence of a desired behavior. Include implementational aspects, that are randomly varied to ensure that evolving individuals will rely on the base set only. Base set features should also be varied from epoch to epoch in order to allow the emergence of robust individuals

13. 13 Robot Intelligence Technology Lab. 3.3 Evolving in simulation Minimal simulations – example Base set features : the wheel motions and the activations of proximity and ambient light sensors while the robot is in the main corridor. Implementational aspect : the activation of proximity sensors during the negotiation of the junction. The T-maze environmentThe minimal simulation of the T-maze

14. 14 Robot Intelligence Technology Lab. 3.4 Fitness space Fitness function plays a major role The results obtained with even slightly different fitness functions are hardly comparable. The choice of a suitable function often proceeds y trials and errors Fitness space An objective framework for describing and comparing fitness functions. Help to define general guidelines for designing fitness function

15. 15 Robot Intelligence Technology Lab. 3.4 Fitness space Fitness space The dimension functional-behavioral  A functional fitness would rate the oscillation frequency of the neurons in order to evolve controllers whose dynamics corresponds to walking patterns.  A behavioral function would rate the distance covered by the robot within a certain time period. The dimension explicit-implicit  The total number of variables, constants, and constraints included in the function. The dimension external-internal  External variables may not be measurable in the real world or may require the realization of complex machineries.

16. 16 Robot Intelligence Technology Lab. 3.4 Fitness space Subjective fitness Human observers rate the performance Biomorphs (Dawkins, 1986) : an observer can select some individuals out of a screen. Usually located in the BEE of the fitness space. Can be useful when it is difficult to design an objective function or in situation where it is desirable to have a machine adapt interactively with a human subject (in entertainment and service robotics). However, if the subject is inaccurate and inconsistent, it may be difficult to obtain meaningful results.