Discovering Motion Flow by Temporal-Informational Correlations in Sensors
- Resource Type
- Conference Paper
- Authors
- Olsson, Lars; Nehaniv, Chrystopher L.; Polani, Daniel
- Source
- Olsson, Lars and Nehaniv, Chrystopher L. and Polani, Daniel (2005) Discovering Motion Flow by Temporal-Informational Correlations in Sensors. [Conference Paper]
- Subject
- Computer Science: Statistical Models
Computer Science: Machine Learning
Computer Science: Robotics
Statistical Models
Machine Learning
Robotics
- Language
A method is presented for adapting the sensors of a robot to its current environment and to learn motion flow detection by observing the informational relations between sensors and actuators. Examples are shown where the robot learns to detect motion flow from sensor data generated by its own movement.