On the nature of robot workspace
International Journal of Robotics Research
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Mathematical Programming: Series A and B
Numerical Methods for Reachable Space Generation of Humanoid Robots
International Journal of Robotics Research
Tracking objects with generic calibrated sensors: An algorithm based on color and 3D shape features
Robotics and Autonomous Systems
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
Implicit Sensorimotor Mapping of the Peripersonal Space by Gazing and Reaching
IEEE Transactions on Autonomous Mental Development
Integration of Active Vision and Reaching From a Developmental Robotics Perspective
IEEE Transactions on Autonomous Mental Development
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We describe a learning strategy that allows a humanoid robot to autonomously build a representation of its workspace: we call this representation Reachable Space Map. Interestingly, the robot can use this map to: (i) estimate the Reachability of a visually detected object (i.e. judge whether the object can be reached for, and how well, according to some performance metric) and (ii) modify its body posture or its position with respect to the object to achieve better reaching. The robot learns this map incrementally during the execution of goal-directed reaching movements; reaching control employs kinematic models that are updated online as well. Our solution is innovative with respect to previous works in three aspects: the robot workspace is described using a gaze-centered motor representation, the map is built incrementally during the execution of goal-directed actions, learning is autonomous and online. We implement our strategy on the 48-DOFs humanoid robot Kobian and we show how the Reachable Space Map can support intelligent reaching behavior with the whole-body (i.e. head, eyes, arm, waist, legs).