Embodied Moving-Target Seeking with Prediction and Planning

We present a bio-inspired control method for moving-target seeking with a mobile robot, which resembles a predator-prey scenario The motor repertoire of a simulated Khepera robot was restricted to a discrete number of ‘gaits' After an exploration phase, the robot automatically synthesizes a model of its motor repertoire, acquiring a forward model Two additional components were introduced for the task of catching a prey robot First, an inverse model to the forward model, which is used to determine the action (gait) needed to reach a desired location Second, while hunting the prey, a model of the prey's behavior is learned online by the hunter robot All the models are learned ab initio, without assumptions, work in egocentric coordinates, and are probabilistic in nature Our architecture can be applied to robots with any physical constraints (or embodiment), such as legged robots.

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

[2]  R. A. Brooks,et al.  Intelligence without Representation , 1991, Artif. Intell..

[3]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[4]  M. Genta,et al.  Infant communication, cognition and their interdependence , 1997 .

[5]  A. Meltzoff,et al.  Explaining Facial Imitation: A Theoretical Model. , 1997, Early development & parenting.

[6]  D. Wolpert,et al.  Internal models in the cerebellum , 1998, Trends in Cognitive Sciences.

[7]  Andy Clark,et al.  Towards a Cognitive Robotics , 1999, Adapt. Behav..

[8]  Rolf Pfeifer,et al.  Understanding intelligence , 2020, Inequality by Design.

[9]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[10]  Rick Grush,et al.  The emulation theory of representation: Motor control, imagery, and perception , 2004, Behavioral and Brain Sciences.

[11]  Olivier Michel,et al.  Cyberbotics Ltd. Webots™: Professional Mobile Robot Simulation , 2004 .

[12]  Lambert Schomaker,et al.  Anticipation in cybernetic systems: a case against mindless anti-representationalism , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[13]  B. Webb.  Neural mechanisms for prediction: do insects have forward models? , 2004, Trends in Neurosciences.

[14]  Y. Demiris,et al.  From motor babbling to hierarchical learning by imitation: a robot developmental pathway , 2005 .

[15]  Michael Tarsitano,et al.  Route selection by a jumping spider (Portia labiata) during the locomotory phase of a detour , 2006, Animal Behaviour.

[16]  Giovanni Pezzulo,et al.  Anticipation and Future-Oriented Capabilities in Natural and Artificial Cognition , 2006, 50 Years of Artificial Intelligence.

[17]  Fumiya Iida,et al.  50 Years of Artificial Intelligence, Essays Dedicated to the 50th Anniversary of Artificial Intelligence , 2007, 50 Years of Artificial Intelligence.

[18]  Richard J. Duro,et al.  On the potential contributions of hybrid intelligent approaches to Multicomponent Robotic System development , 2010, Inf. Sci..

[19]  Alejandro Dizan Vasquez Govea,et al.  Incremental Learning for Motion Prediction of Pedestrians and Vehicles , 2010, Springer Tracts in Advanced Robotics.