Incremental Robot Shaping

We propose a modular architecture for autonomous robots which allows for the implementation of basic behavioral modules by both programming and training, and accommodates for an evolutionary development of the interconnections among modules. This architecture can implement highly complex controllers and allows for incremental shaping of the robot behavior. Our proposal is exemplified and evaluated experimentally through a number of mobile robotic tasks involving exploration, battery recharging and object manipulation.

[1]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[2]  David H. Ackley,et al.  Generalization and Scaling in Reinforcement Learning , 1989, NIPS.

[3]  Rodney A. Brooks,et al.  Elephants don't play chess , 1990, Robotics Auton. Syst..

[4]  M. Dorigo,et al.  Organisation of robot behaviour through genetic learning processes , 1991, Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments.

[5]  Rodney A. Brooks,et al.  Artificial Life and Real Robots , 1992 .

[6]  Marco Dorigo,et al.  Genetics-based machine learning and behavior-based robotics: a new synthesis , 1993, IEEE Trans. Syst. Man Cybern..

[7]  Francesco Mondada,et al.  Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms , 1993, ISER.

[8]  Dario Floreano.  Emergence of nest-based foraging strategies in ecosystems of neural networks , 1993 .

[9]  Robert A. Jacobs,et al.  Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.

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

[11]  Francesco Mondada,et al.  Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot , 1994 .

[12]  Marco Colombetti,et al.  Robot Shaping: Developing Autonomous Agents Through Learning , 1994, Artif. Intell..

[13]  Inman Harvey,et al.  Seeing the Light: Artiicial Evolution, Real Vision Seeing the Light: Artiicial Evolution, Real Vision , 1994 .

[14]  Ulrich Nehmzow,et al.  Achieving rapid adaptations in robots by means of external tuition , 1994 .

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

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

[17]  Marco Colombetti,et al.  Behavior analysis and training-a methodology for behavior engineering , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[18]  Marco Colombetti,et al.  Robot Shaping: An Experiment in Behavior Engineering , 1997 .

[19]  Stefano Nolfi,et al.  Using Emergent Modularity to Develop Control Systems for Mobile Robots , 1997, Adapt. Behav..

[20]  Dario Floreano,et al.  Reducing Human Design and Increasing Adaptivity in Evolutionary Robotics , 1997 .

[21]  Stefano Nolfi.  Evolving non-trivial behavior on autonomous robots: Adaptation is more powerful than decomposition a , 1997 .

[22]  David S. Touretzky,et al.  Operant Conditioning in Skinnerbots , 1997, Adapt. Behav..

[23]  D. Floreano,et al.  Co-evolving predator and prey robots : Do ‘ arms races ’ ar ise in ar tificial evolution ? , 1998 .

[24]  Stefano Nolfi,et al.  Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems , 1998, Neural Networks.

[25]  David OrenApril.  The in uence of learning on evolutionPresented by , .