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Adaptive Behavior
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Evolving the Neural Controller for a Robotic Arm Able to Grasp Objects on the Basis of Tactile Sensors

Raffaele Bianco

Stefano Nolfi

Institute of Cognitive Science and Technologies, National Research Council (CNR)

We describe the results of a set of evolutionary experiments in which a simulated robotic arm provided with a two-fingered hand has to reach and grasp objects with different shapes and orientations on the basis of simple tactile information. The results that we obtained are encouraging and demonstrate that the problem of grasping objects with characteristics that vary within a certain range can be solved by producing rather simple forms of behavior. These forms of behavior exploit emergent characteristics of the interaction between the body of the robot, its control system, and the environment. In particular we show that evolved individuals do not try to keep the environment stable, but rather push and pull the objects; thus, they produce a dynamic in the environment and exploit the interaction between the body of the robot and the dynamic environment to master different environmental conditions with similar control strategies.

Key Words: evolutionary robotics • robotic arm • sensory–motor coordination

Adaptive Behavior, Vol. 12, No. 1, 37-45 (2004)
DOI: 10.1177/105971230401200102
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R. Nishimoto, J. Namikawa, and J. Tani
Learning Multiple Goal-Directed Actions Through Self-Organization of a Dynamic Neural Network Model: A Humanoid Robot Experiment
Adaptive Behavior, April 1, 2008; 16(2-3): 166 - 181.
[Abstract] [PDF]