Using Emergent Modularity to Develop Control Systems for Mobile Robots

Abstract

A new way of building control systems, known as behavior-based robotics, has recently been proposed to overcome the difficulties of the traditional artificial intelligence approach to robotics. This new approach is based on the idea of providing the robot with a range of simple behaviors and letting the environment determine which behavior should have control at any given time. We will present a set of experiments in which neural networks with different architectures have been trained to control a mobile robot designed to keep an arena clear by picking up trash objects and releasing them outside the arena. Controller weights are selected using a form of genetic algorithm and do not change during the lifetime (i.e., no learning occurs). We will compare, in simulation and on a real robot, five different network architectures and will show that a network that allows for fine-grained modularity achieves significantly better performance. By comparing the functionality of each network module and its interaction with a description of the simple behavior components, we will show that it is not possible to find simple correlations; rather, module switching and interaction are correlated with low-level sensorimotor mappings. This implies that the engineering-oriented approach to behavior-based robotics might have serious limitations because it is difficult to know in advance the appropriate mappings between behavior components and sensorimotor activity for complex tasks.

Keywords

autonomous robots behavior-based robotics modularity neural networks genetic algorithms

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References

Ackley, D.H. , & Littman, M.L. (1991). Interactions between learning and evolution. In C. G. Langton , J. D. Farmer , S. Rasmussen & C. E. Taylor (Eds.), Artificial life II. Reading, MA: Addison-Wesley

Braitenberg, V. (1984). Vehicles: Experiments in synthetic psychology . Cambridge, MA: MIT Press.

Brooks, R.A. (1986). A robust layered control system for a mobile robot . IEEE Journal of Robotics and Autonomation, 2, 14-23.

Cliff, D.T. , Harvey, I. , & Husbands, P. (1993). Explorations in evolutionary robotics. Adaptive Behavior, 2, 73-110.

Colombetti, M. , Dorigo, M. , & Borghi, G. (1996). Behavior analysis and training. A methodology for behavior engineering. IEEE Transactions on Systems, Man, and Cybernetics [B], 26(1), 29-41.

Dorigo, M. , & Schnepf, U. (1993). Genetic-based machine learning and behaviour based robotics: A new synthesis. IEEE Transaction on Systems, Man, and Cybernetics, 23(1), 141-154.

Elman, J.L. (1990). Finding structure in time. Cognitive Science, 14, 179-211.

Floreano, D. , & Mondada, F. (1996). Evolution of plastic neurocontrollers for situated agents. In P Maes , M. Mataric , J.-A. Meyer , J. Pollack & S. Wilson (Eds.), From animals to animats IV. Cambridge, MA: MIT Press.

Gruau, F. (1995). Automatic definition of modular neural networks . Adaptive Behavior, 2, 151-183.

10.

Holland, J.H. (1975). Adaptation in natural and artificial systems, Ann Arbor: University of Michigan Press .

11.

Maes, P. (1992). Learning behavior networks from experience. In F. J. Varela & P. Bourgine (Eds.), Toward a practice of autonomous systems: Proceedings of the First European Conference on Artificial Life, Cambridge, MA: MIT Press/Bradford Books .

12.

Mahadevan, S. , & Connell, J. (1992). Automatic programming of behavior-based robots using reinforcement learning. Artificial Intelligence, 55, 311-365.

13.

Mataric, M.J. (1992). Behavior-based control: Main properties and implications . Proceedings of the IEEE International Conference on Robotics and Automation, Workshop on Architectures for Intelligent Control Systems, Nice, France.

14.

Mataric, M.J. , & Cliff, D. (in press). Challenges in evolving controllers for physical robots . Robotics and Autonomous Systems, special issue.

15.

Miglino, O. , Lund, H.H. , & Nolfi, S. (1995). Evolving mobile robots in simulated and real environments . Artificial Life, 2(4), 417-434.

16.

Mondada, F. , Franzi, E. , & Ienne, P. (1993). Mobile robot miniaturisation: A tool for investigation in control algorithms. In Proceedings of the Third International Symposium on Experimental Robotics, Kyoto , Japan.

17.

Montana, D.J. , & Davis, L. (1989). Training feedforward neural networks using genetic algorithms. In N. S. Sridhavan (Eds.), Proceedings of the Eleventh Joint Conference on Artificial Intelligence, Vol. 1. Palo Alto, CA: Morgan Kaufmann.

18.

Nolfi, S. (1996). Adaptation as a more powerful tool than decomposition and integration. In T. Fogarty & G. Venturini (Eds.), Proceedings of the Workshop on Evolutionary Computing and Machine Learning, Thirteenth International Conference on Machine Learning, Bari, Italy.

19.

Nolfi, S. (in press). Evolving non-trivial behaviors on real robots: A garbage collecting robot. Robotics and Autonomous Systems.

20.

Nolfi, S. , Elman, J.L. , & Parisi, D. (1994). Learning and evolution in neural networks. Adaptive Behavior, 1, 5-28.

21.

Nolfi, S. , Floreano, D. , Miglino, O. , & Mondada, F. (1994). How to evolve autonomous robots: Different approaches in evolutionary robotics. In R. A. Brooks & P Maes (Eds.), Proceedings of the Fourth International Conference on Arificial Life. Cambridge, MA: MIT Press.

22.

Nolfi, S. , Miglino, O. , & Parisi, D. (1994). Phenotypic plasticity in evolving neural networks . In D. P Gaussier & J.-D. Nicoud (Eds.), Proceedings of the International Conference from Perception to Action. Los Alamitos, CA: IEEE Press.

23.

Nolfi, S. , & Parisi, D. (1993). Self-selection of input stimuli for improving performance. In G. A. Bekey (Ed.), Neural networks and robotics. Norwell, MA: Kluwer Academic.

24.

Parisi, D. , Cecconi, F. , & Nolfi, S. (1990). Econets: Neural networks that learn in an environment . Network, 1, 149-168.

25.

Scheier, C. , & Pfeifer, R. (1995). Classification as sensory-motor coordination: A case study on autonomous agents. In F. Moran , A. Moreno, J. J. Merelo & P. Chacon (Eds.), Advances in artificial life: Proceedings of the Third European Conference on Artificial Life. Berlin: Springer-Verlag.

26.

Scheier, C. , & Lambrinos, D. (1995). Adaptive classification in autonomous agents (Tech. Rep.) Zurich: AI Laboratory, Computer Science Department, University of Zurich.

27.

Sharkey, N.E. , & Heemskerk, N.H. (in press). The neural mind and the robot. In A. J. Browne (Ed.), Current perspective in neural computing. London: IOP Press.