Using sensor habituation in mobile robots to reduce oscillatory movements in narrow corridors

Habituation is a form of nonassociative learning observed in a variety of species of animals. Arguably, it is the simplest form of learning. Nonetheless, the ability to habituate to certain stimuli implies plastic neural systems and adaptive behaviors. This paper describes how computational models of habituation can be applied to real robots. In particular, we discuss the problem of the oscillatory movements observed when a Khepera robot navigates through narrow hallways using a biologically inspired neurocontroller. Results show that habituation to the proximity of the walls can lead to smoother navigation. Habituation to sensory stimulation to the sides of the robot does not interfere with the robot's ability to turn at dead ends and to avoid obstacles outside the hallway. This paper shows that simple biological mechanisms of learning can be adapted to achieve better performance in real mobile robots.

[1]  P. Groves,et al.  Habituation: a dual-process theory. , 1970, Psychological review.

[2]  Irving Kupfermann,et al.  Neuronal Correlates of Habituation and Dishabituation of the Gill-Withdrawal Reflex in Aplysia , 1970, Science.

[3]  V. Braitenberg.  Vehicles, Experiments in Synthetic Psychology , 1984 .

[4]  L. A. Akers,et al.  VLSI implementation of rehabituation , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[5]  Lisa Meeden,et al.  An incremental approach to developing intelligent neural network controllers for robots , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[6]  S. Grossberg,et al.  Neural dynamics of attentionally modulated Pavlovian conditioning: Conditioned reinforcement, inhibition, and opponent processing , 1987, Psychobiology.

[7]  Nancy Forbes.  Imitation of Life , 2004 .

[8]  N. Weinberger,et al.  Habituation produces frequency-specific plasticity of receptive fields in the auditory cortex. , 1991, Behavioral neuroscience.

[9]  Ben J. A. Kröse,et al.  Distributed adaptive control: The self-organization of structured behavior , 1992, Robotics Auton. Syst..

[10]  S. Grossberg.  A psychophysiological theory of reinforcement, drive, motivation, and attention , 1987 .

[11]  J. C. Stanley.  Computer simulation of a model of habituation , 1976, Nature.

[12]  Jean-Arcady Meyer,et al.  Lego Vehicles: A Technology for Studying Intelligent Systems , 1991 .

[13]  Francesco Mondada,et al.  Evolution of homing navigation in a real mobile robot , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[14]  S. Grossberg,et al.  Neural dynamics of attentionally modulated Pavlovian conditioning: blocking, interstimulus interval, and secondary reinforcement. , 1987, Applied optics.

[15]  E. Kandel,et al.  Molecular biology of learning: modulation of transmitter release. , 1982, Science.

[16]  Philippe Gaussier,et al.  A topological neural map for on-line learning: emergence of obstacle avoidance in a mobile robot , 1994 .

[17]  José del R. Millán,et al.  Rapid, safe, and incremental learning of navigation strategies , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[18]  Rodney A. Brooks,et al.  Intelligence Without Reason , 1991, IJCAI.

[19]  Maja J. Mataric,et al.  Integration of representation into goal-driven behavior-based robots , 1992, IEEE Trans. Robotics Autom..

[20]  Paolo Gaudiano,et al.  Application of Biological Learning Theories to Mobile Robot Avoidance and Approach Behaviors , 1998, Adv. Complex Syst..

[21]  David S. Barrett,et al.  SQUIRT: The Prototypical Mobile Robot for Autonomous Graduate Students , 1989 .

[22]  Paolo Gaudiano,et al.  Adaptive obstacle avoidance with a neural network for operant conditioning: experiments with real robots , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[23]  Paul F. M. J. Verschure,et al.  Collision avoidance using a model of the locust LGMD neuron , 2000, Robotics Auton. Syst..

[24]  Ulrich Nehmzow,et al.  Flexible Control of Mobile Robots through Autonomous Competence Acquisition , 1995 .

[25]  Ronald C. Arkin,et al.  Implementing schema-theoretic models of animal behavior in robotic systems , 1998, AMC'98 - Coimbra. 1998 5th International Workshop on Advanced Motion Control. Proceedings (Cat. No.98TH8354).

[26]  Piero Mussio,et al.  Toward a Practice of Autonomous Systems , 1994 .

[27]  R. F. Tredgold,et al.  The Living Brain , 1954, Mental Health.

[28]  Tim Smithers,et al.  Lego vehicles: a technology for studying intelligent systems , 1991 .

[29]  Stefano Nolfi,et al.  How to Evolve Autonomous Robots: Different Approaches in Evolutionary Robotics , 1994 .

[30]  Ulrich Nehmzow,et al.  Detecting Novel Features of an Environment Using Habituation , 2000 .

[31]  E R Kandel,et al.  Neuronal Mechanisms of Habituation and Dishabituation of the Gill-Withdrawal Reflex in Aplysia , 1970, Science.

[32]  N. Franceschini,et al.  From insect vision to robot vision , 1992 .

[33]  V. Braitenberg,et al.  Taxis, kinesis and decussation. , 1965, Progress in brain research.

[34]  M. Davis,et al.  A primary acoustic startle circuit: lesion and stimulation studies , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[35]  W. Walter.  A Machine that Learns , 1951 .

[36]  Stephen Grossberg,et al.  A Neural Theory of Punishment and Avoidance , II : Quantitative Theory , 2003 .

[37]  Carolina Chang,et al.  Improving hallway navigation in mobile robots with sensor habituation , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.