Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural Computation
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Neural Networks - Special issue on organisation of computation in brain-like systems
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Incremental learning of complex temporal patterns
IEEE Transactions on Neural Networks
Learning Semantic Combinatoriality from the Interaction between Linguistic and Behavioral Processes
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Motor primitive and sequence self-organization in a hierarchical recurrent neural network
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
On Cognition as Dynamical Coupling: An Analysis of Behavioral Attractor Dynamics
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Coordinating with the Future: The Anticipatory Nature of Representation
Minds and Machines
Schema-Based Design and the AKIRA Schema Language: An Overview
Anticipatory Behavior in Adaptive Learning Systems
Anticipatory Behavior in Adaptive Learning Systems
Advances in Artificial Neural Systems
The Cognitive Body: From Dynamic Modulation to Anticipation
Anticipatory Behavior in Adaptive Learning Systems
Autonomy of Self at Criticality: The Perspective from Synthetic Neuro-Robotics
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Intelligence Dynamics: a concept and preliminary experiments for open-ended learning agents
Autonomous Agents and Multi-Agent Systems
Spatio-temporal memories for machine learning: a long-term memory organization
IEEE Transactions on Neural Networks
An analysis of behavioral attractor dynamics
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Anticipation and future-oriented capabilities in natural and artificial cognition
50 years of artificial intelligence
Taming the beast: guided self-organization of behavior in autonomous robots
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Imitating others by composition of primitive actions: A neuro-dynamic model
Robotics and Autonomous Systems
On the Development of an Ants-Inspired Navigational Network for Autonomous Robots
International Journal of Intelligent Mechatronics and Robotics
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A novel hierarchical neural network architecture for sensory-motor learning and behavior generation is proposed. Two levels of forward model neural networks are operated on different time scales while parametric interactions are allowed between the two network levels in the bottom-up and top-down directions. The models are examined through experiments of behavior learning and generation using a real robot arm equipped with a vision system. The results of the learning experiments showed that the behavioral patterns are learned by self-organizing the behavioral primitives in the lower level and combining the primitives sequentially in the higher level. The results contrast with prior work by Pawelzik et al. [Neural Comput. 8 (1996) 340], Tani and Nolfi [From animals to animats, 1998], and Wolpert and Kawato [Neural Networks 11 (1998) 1317] in that the primitives are represented in a distributed manner in the network in the present scheme whereas, in the prior work, the primitives were localized in specific modules in the network. Further experiments of on-line planning showed that the behavior could be generated robustly against a background of real world noise while the behavior plans could be modified flexibly in response to changes in the environment. It is concluded that the interaction between the bottom-up process of recalling the past and the top-down process of predicting the future enables both robust and flexible situated behavior.