Learning to feel the physics of a body

Despite the tremendous progress in robotic hardware and in both sensorial and computing efficiencies the performance of contemporary autonomous robots is still far below that of simple animals. This has triggered an intensive search for alternative approaches to the control of robots. The present paper exemplifies a general approach to the self-organization of behavior which has been developed and tested in various examples in recent years. We apply this approach to an underactuated "snake" like artifact with a complex physical behavior which is not known to the controller. Due to the weak forces available, the controller so to say has to develop a kind of feeling for the body which is seen to emerge from our approach in a natural way with meandering and rotational collective modes being observed in computer simulation experiments

[1]  Randall D. Beer,et al.  A Dynamical Systems Perspective on Agent-Environment Interaction , 1995, Artif. Intell..

[2]  Yasuhiro Fukuoka,et al.  Adaptive Dynamic Walking of a Quadruped Robot on Irregular Terrain Based on Biological Concepts , 2003, Int. J. Robotics Res..

[3]  Matthew M. Williamson,et al.  Neural control of rhythmic arm movements , 1998, Neural Networks.

[4]  Shinya Kotosaka,et al.  Synchronized Robot Drumming by Neural Oscillator , 2001 .

[5]  Jun Morimoto,et al.  Learning from demonstration and adaptation of biped locomotion , 2004, Robotics Auton. Syst..

[6]  Jun Nakanishi,et al.  Learning composite adaptive control for a class of nonlinear systems , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[7]  山田 祐,et al.  Open Dynamics Engine を用いたスノーボードロボットシミュレータの開発 , 2007 .

[8]  Ralf Der,et al.  Rocking Stamper and Jumping Snakes from a Dynamical Systems Approach to Artificial Life , 2006, Adapt. Behav..

[9]  Katsuhisa Furuta,et al.  Juggling control using neural oscillators , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

[10]  Ralf Der,et al.  Contingent robot behaviors from self-referential dynamical systems , 2005 .

[11]  R. Der,et al.  True autonomy from self-organized adaptivity , 2002 .

[12]  Kiyotoshi Matsuoka,et al.  Sustained oscillations generated by mutually inhibiting neurons with adaptation , 1985, Biological Cybernetics.

[13]  Robert F. Port,et al.  MIND IN MOTION: , 2019, Dune.

[14]  Kazunori Hase,et al.  Computational evolution of human bipedal walking by a neuro-musculo-skeletal model , 1999, Artificial Life and Robotics.

[15]  Ralf Der,et al.  Self-Organized Exploration and Automatic Sensor Integration From the Homeokinetic Principle , 2004 .