Learning to fall: Designing low damage fall sequences for humanoid soccer robots
Robotics and Autonomous Systems
Three-dimensional face pose detection and tracking using monocular videos: tool and application
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
A probabilistic model of overt visual attention for cognitive robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A nonparametric Bayesian approach toward robot learning by demonstration
Robotics and Autonomous Systems
Active learning of inverse models with intrinsically motivated goal exploration in robots
Robotics and Autonomous Systems
Learning by observation of agent software images
Journal of Artificial Intelligence Research
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In this paper, we present a strategy whereby a robot acquires the capability to learn by imitation following a developmental pathway consisting on three levels: 1) sensory-motor coordination; 2) world interaction; and 3) imitation. With these stages, the system is able to learn tasks by imitating human demonstrators. We describe results of the different developmental stages, involving perceptual and motor skills, implemented in our humanoid robot, Baltazar. At each stage, the system's attention is drawn toward different entities: its own body and, later on, objects and people. Our main contributions are the general architecture and the implementation of all the necessary modules until imitation capabilities are eventually acquired by the robot. Also, several other contributions are made at each level: learning of sensory-motor maps for redundant robots, a novel method for learning how to grasp objects, and a framework for learning task description from observation for program-level imitation. Finally, vision is used extensively as the sole sensing modality (sometimes in a simplified setting) avoiding the need for special data-acquisition hardware