Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Robust sensor-based grasp primitive for a three-finger robot hand
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Active categorical perception of object shapes in a simulated anthropomorphic robotic arm
IEEE Transactions on Evolutionary Computation
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
IEEE Transactions on Autonomous Mental Development
Evolving artificial neural network ensembles
IEEE Computational Intelligence Magazine
Integrating Language and Cognition: A Cognitive Robotics Approach
IEEE Computational Intelligence Magazine
Learning the combinatorial structure of demonstrated behaviors with inverse feedback control
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
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In this paper, we show how a simulated humanoid robot controlled by an artificial neural network can acquire the ability to manipulate spherical objects located over a table by reaching, grasping, and lifting them. The robot controller is developed through an adaptive process in which the free parameters encode the control rules that regulate the fine-grained interaction between the agent and the environment, and the variations of these free parameters are retained or discarded on the basis of their effects at the level of the behavior exhibited by the agent. The robot develops the sensory-motor coordination required to carry out the task in two different conditions; that is, with or without receiving as input a linguistic instruction that specifies the type of behavior to be exhibited during the current phase. The obtained results shown that the linguistic instructions facilitate the development of the required behavioral skills.