- Email: [email protected]
Moving the frontiers between robotics and biology
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Robotics and Autonomous Systems Robotics and Autonomous Systems 16 (1995) v-vii
Editorial
Moving the Frontiers between Robotics and Biology P h i l i p p e G a u s s i e r a,., S t ~ p h a n e Z r e h e n b a ENSEA ETIS, 6 av du Ponceau, 95014 Cergy Pontoise Cedex, France b Laboratoire de Microinformatique, EPFL-DI, CH-1015 Lausanne, Switzerland
This special issue follows the Perception-Action conference PerAc'94 organised by P. Gaussier and J.D. Nicoud at the EPFL in Switzerland. The conference proposed a state of the art of techniques related to the control of autonomous robots interacting with an unknown world. The general theme was about the study of how actions can help to modify perception in an interesting manner. Emphasis was on 'technological transfer' from biology (ethology, neurobiology . . . ) and psychology to engineering. Through the different papers of this issue, we try to address the following reductionist questions: what can roboticists take from biology and what can the robots bring to the biologists? The papers are approximately classified according to the complexity of the cognitive task they address. From precise description of sensory-motor controllers to higher level cognitive systems. The key idea is to process in the same way as phylogenesis: from simple to more complex mechanisms to control the animat brain and its behaviors. In the first paper, J. Stewart settles the philosophical frame of the constructivist approach we want to promote. He opposes it to the computationalist theory and shows that we should try to only constrain the robot behavior through proscriptive constraints rather than prescriptive ones
* E-mail: [email protected] or [email protected].
and he concludes by demonstrating that "abstract categories" can only be built while acting in the environment. The next two papers by B. Webb and C. Ferrel address problems linked to the understanding of insects' behaviors. B. Webb uses robotics experiments as a test of a neuroethological theory. She uses a robot as a simulation of a cricket. Her robotics simulations show how crickets can locate sounds emitted by other crickets and use these sounds to meet fellow creatures. This nicely illustrates the interest for the biologist to use robots as animal models. C. Ferrel presents an engineering point of view for the control of walking robots based on insect models of locomotion. She discusses the interest of reflexive versus patterned controllers in the biological and engineering perspective. Both papers show that biology and robotics have a lot of things to bring to each other, but that we should be aware of the differences in their technological constraints leading to possibly different optimal solutions. The possibility of evolving neural networks to control robots is investigated in the next two papers. J. Kodjabachian and J.A. Meyer present an overview of Genetic Algorithms (GA) for building animats. They explain that most recent algorithms try to take into account the ontogenesis of the robots and of their neural brains. They insist on evolutionary and development processes and on their links with theoretical biology (these
0921-8890/95/$09.50 © 1995 Elsevier Science B.V. All rights reserved SSDI 0 9 2 1 - 8 8 9 0 ( 9 5 ) 0 0 0 7 2 - 0
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P. Gaussier, S. Zrehen / Robotics and Autonomous Systems 16 (1995) v-vii
algorithms allow to test evolutionary theories). The paper by F. Mondada and D. Floreano is devoted to the application of G A to real robots. The authors explain how to simulate with a single real robot the evolution of a population of robots with hardwired neural networks that aim at behaviors such as obstacle avoidance, homing and grasping. The following papers are interested in more cognitive behaviors that involve learning or adaptation during the life time. G. Sch6ner, M. Dose and C. Engels address the problem of the lack of a unifying theoretical language to describe the robot behaviors. They propose an outline of a quantitative theory of behavior dynamics through a neural field representation. They apply their theory to a robot that uses optical flow information for obstacle avoidance and dead reckoning. P. Verschure, J. Wray, O. Spons, G. Tononi and G. Edelman are involved in the robotic study of a neurobiologically plausible neural conditioning algorithm. Their model is successfully applied to a block-sorting task performed by a robot equipped with a CCD color camera. This paper is followed by two other papers linked to place recognition, also using biologically inspired neural networks. But for reasons of computer capabilities and biological understanding, the simulations are settled at a functional level. I. Bachelder and A. Waxman propose a hierarchical integrated system that allows a robot to recognize objects as well as to learn places in the environment. The system is able to build a map of a room according to visual cues. At the opposite, P. Gaussier and S. Zrehen propose a system that does not need to create a map of the environment. They focus on the problem of building an architecture that allows a robot to navigate autonomously and to learn how to reach a goal according to its own motivations. The last two papers investigate how several robots can cooperate for a given task. M. Matari6 proposes an overview of the multi-agent problematic (communication and cooperation, interfere n c e a n d conflict) and she shows applications that use behavior-based control (following, homing, flocking and foraging tasks), At last, K. Dautenhahn explores the interest of social interactions
through the imitation mechanism. She addresses problems such as social intelligence, communication and body image. She applies these concepts to real robots that learn to imitate the behaviors of others for a hill climbing task that needs the cooperation of several robots. By contrast, the paper of R. Chatila inserted at the middle of this special issue shows what "classical" Artificial Intelligence succeeds in doing and what we must challenge to pass from our toy problems to real size applications. The author describes in a top down manner the different abstraction levels necessary to control a mobile robot in an unknown environment. A CCD camera and a laser beam are used on his robot to extract tridimensional information in visual scenes. A Cartesian map of its environment is constructed and at the same time a goal indicated by a human operator can be reached. As a conclusion, all these papers show that we assist in a maturation of the field of the animat approach and of the neural techniques to control them. Their main success is due to the capability to build complete systems in a constructivist perspective. We are now able to realize efficient low level controllers and on-line learning of complex perceptual stimuli and their association to actions. The new areas to investigate should address the topic of problem complexity classification. In the same way, we must succeed to by-pass the limitations of classical reinforcement techniques and gregarious evolution mechanisms. Access to higher level tasks will depend on the robot capability to interact and to communicate with humans and with other robots and will lead to imagine new efficient mechanisms for learning by imitation (recognition of other fellow creatures, categorization of the observed sensory motor sequences, etc.).
Acknowledgements We would like to thank the Micro Computing Laboratory (LAMI) at the Polytechnical Federal Institute of Lausanne in Switzerland (EPFL) for the organization of the PerAc conference and the
P. Gaussier, S. Zrehen / Robotics and Autonomous Systems 16 (1995) v-vii
Laboratory of Image and Signal Analysis (ETIS) at the National Engineering School in Electronics and its Applications in France (ENSEA) for supporting the realization of the special issue. We would also like to thank all the reviewers of the
vii
papers for their careful work and all our friends at ENSEA and LAMI for their precious help during the different stages of the elaboration of this special issue.
Robotics and Autonomous Systems Robotics and Autonomous Systems 16 (1995) v-vii
Editorial
Moving the Frontiers between Robotics and Biology P h i l i p p e G a u s s i e r a,., S t ~ p h a n e Z r e h e n b a ENSEA ETIS, 6 av du Ponceau, 95014 Cergy Pontoise Cedex, France b Laboratoire de Microinformatique, EPFL-DI, CH-1015 Lausanne, Switzerland
This special issue follows the Perception-Action conference PerAc'94 organised by P. Gaussier and J.D. Nicoud at the EPFL in Switzerland. The conference proposed a state of the art of techniques related to the control of autonomous robots interacting with an unknown world. The general theme was about the study of how actions can help to modify perception in an interesting manner. Emphasis was on 'technological transfer' from biology (ethology, neurobiology . . . ) and psychology to engineering. Through the different papers of this issue, we try to address the following reductionist questions: what can roboticists take from biology and what can the robots bring to the biologists? The papers are approximately classified according to the complexity of the cognitive task they address. From precise description of sensory-motor controllers to higher level cognitive systems. The key idea is to process in the same way as phylogenesis: from simple to more complex mechanisms to control the animat brain and its behaviors. In the first paper, J. Stewart settles the philosophical frame of the constructivist approach we want to promote. He opposes it to the computationalist theory and shows that we should try to only constrain the robot behavior through proscriptive constraints rather than prescriptive ones
* E-mail: [email protected] or [email protected].
and he concludes by demonstrating that "abstract categories" can only be built while acting in the environment. The next two papers by B. Webb and C. Ferrel address problems linked to the understanding of insects' behaviors. B. Webb uses robotics experiments as a test of a neuroethological theory. She uses a robot as a simulation of a cricket. Her robotics simulations show how crickets can locate sounds emitted by other crickets and use these sounds to meet fellow creatures. This nicely illustrates the interest for the biologist to use robots as animal models. C. Ferrel presents an engineering point of view for the control of walking robots based on insect models of locomotion. She discusses the interest of reflexive versus patterned controllers in the biological and engineering perspective. Both papers show that biology and robotics have a lot of things to bring to each other, but that we should be aware of the differences in their technological constraints leading to possibly different optimal solutions. The possibility of evolving neural networks to control robots is investigated in the next two papers. J. Kodjabachian and J.A. Meyer present an overview of Genetic Algorithms (GA) for building animats. They explain that most recent algorithms try to take into account the ontogenesis of the robots and of their neural brains. They insist on evolutionary and development processes and on their links with theoretical biology (these
0921-8890/95/$09.50 © 1995 Elsevier Science B.V. All rights reserved SSDI 0 9 2 1 - 8 8 9 0 ( 9 5 ) 0 0 0 7 2 - 0
vi
P. Gaussier, S. Zrehen / Robotics and Autonomous Systems 16 (1995) v-vii
algorithms allow to test evolutionary theories). The paper by F. Mondada and D. Floreano is devoted to the application of G A to real robots. The authors explain how to simulate with a single real robot the evolution of a population of robots with hardwired neural networks that aim at behaviors such as obstacle avoidance, homing and grasping. The following papers are interested in more cognitive behaviors that involve learning or adaptation during the life time. G. Sch6ner, M. Dose and C. Engels address the problem of the lack of a unifying theoretical language to describe the robot behaviors. They propose an outline of a quantitative theory of behavior dynamics through a neural field representation. They apply their theory to a robot that uses optical flow information for obstacle avoidance and dead reckoning. P. Verschure, J. Wray, O. Spons, G. Tononi and G. Edelman are involved in the robotic study of a neurobiologically plausible neural conditioning algorithm. Their model is successfully applied to a block-sorting task performed by a robot equipped with a CCD color camera. This paper is followed by two other papers linked to place recognition, also using biologically inspired neural networks. But for reasons of computer capabilities and biological understanding, the simulations are settled at a functional level. I. Bachelder and A. Waxman propose a hierarchical integrated system that allows a robot to recognize objects as well as to learn places in the environment. The system is able to build a map of a room according to visual cues. At the opposite, P. Gaussier and S. Zrehen propose a system that does not need to create a map of the environment. They focus on the problem of building an architecture that allows a robot to navigate autonomously and to learn how to reach a goal according to its own motivations. The last two papers investigate how several robots can cooperate for a given task. M. Matari6 proposes an overview of the multi-agent problematic (communication and cooperation, interfere n c e a n d conflict) and she shows applications that use behavior-based control (following, homing, flocking and foraging tasks), At last, K. Dautenhahn explores the interest of social interactions
through the imitation mechanism. She addresses problems such as social intelligence, communication and body image. She applies these concepts to real robots that learn to imitate the behaviors of others for a hill climbing task that needs the cooperation of several robots. By contrast, the paper of R. Chatila inserted at the middle of this special issue shows what "classical" Artificial Intelligence succeeds in doing and what we must challenge to pass from our toy problems to real size applications. The author describes in a top down manner the different abstraction levels necessary to control a mobile robot in an unknown environment. A CCD camera and a laser beam are used on his robot to extract tridimensional information in visual scenes. A Cartesian map of its environment is constructed and at the same time a goal indicated by a human operator can be reached. As a conclusion, all these papers show that we assist in a maturation of the field of the animat approach and of the neural techniques to control them. Their main success is due to the capability to build complete systems in a constructivist perspective. We are now able to realize efficient low level controllers and on-line learning of complex perceptual stimuli and their association to actions. The new areas to investigate should address the topic of problem complexity classification. In the same way, we must succeed to by-pass the limitations of classical reinforcement techniques and gregarious evolution mechanisms. Access to higher level tasks will depend on the robot capability to interact and to communicate with humans and with other robots and will lead to imagine new efficient mechanisms for learning by imitation (recognition of other fellow creatures, categorization of the observed sensory motor sequences, etc.).
Acknowledgements We would like to thank the Micro Computing Laboratory (LAMI) at the Polytechnical Federal Institute of Lausanne in Switzerland (EPFL) for the organization of the PerAc conference and the
P. Gaussier, S. Zrehen / Robotics and Autonomous Systems 16 (1995) v-vii
Laboratory of Image and Signal Analysis (ETIS) at the National Engineering School in Electronics and its Applications in France (ENSEA) for supporting the realization of the special issue. We would also like to thank all the reviewers of the
vii
papers for their careful work and all our friends at ENSEA and LAMI for their precious help during the different stages of the elaboration of this special issue.