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Behavior-Based Robotics
TLDR
Whence behaviour? animal behaviour robot behaviour behaviour based architectures representational issues for behavioural systems hybrid deliberative/rective architectures perceptual basis for behaviour-based control adaptive behaviour social behaviour fringe robotics - beyond behaviour.Abstract:
Whence behaviour? animal behaviour robot behaviour behaviour-based architectures representational issues for behavioural systems hybrid deliberative/rective architectures perceptual basis for behaviour-based control adaptive behaviour social behaviour fringe robotics - beyond behaviour.read more
Citations
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Journal ArticleDOI
Fuzzy Qualitative Robot Kinematics
TL;DR: A fuzzy qualitative version of robot kinematics is proposed with the goal of bridging the gap between symbolic or qualitative functions and numerical sensing and control tasks for intelligent robotics and an aggregation operator to extract robot behaviors is proposed.
Journal ArticleDOI
Understanding mirror neurons.
TL;DR: These experiments show that motor information can have a signifi cant role in action interpretation and that a mirror-like representation can be developed autonomously as a result of the interaction between an individual and the environment.
Proceedings ArticleDOI
On collective motion in sensor networks: sample problems and distributed algorithms
TL;DR: Some challenges in modeling of robotic networks, motion coordination algorithms, sensing and estimation tasks, and complexity of distributed algorithms in the context of multi-vehicle and sensor networks are discussed.
Proceedings ArticleDOI
Human-robot interaction through spoken language dialogue
TL;DR: The "body and soul" of CARL (Communication, Action, Reasoning and Learning), a robot currently under construction in the laboratory, are presented and it is argued that, for the common user, the only sufficiently practical interface is spoken language.
Journal ArticleDOI
Computational intelligence for structured learning of a partner robot based on imitation
TL;DR: This paper proposes structured learning for a partner robot based on the interactive teaching mechanism and is composed of a spiking neural network, self-organizing map, steady-state genetic algorithm, and softmax action selection.