Sensory prediction for autonomous robots

For a complex autonomous robotic system such as a humanoid robot, the learning-based sensory prediction is considered effective to develop a perceptual environment model by itself. We developed a learning system for an autonomous robot to predict the next sensory information from the current sensory information and the expected action. The system we consider contains a learning procedure and a behavior generation procedure. The learning procedure uses a multi layer perceptron minimizing the error between a given sensory input and its predicted value. The behavior generation procedure is based on a uniform probablistic density function to sample the learning data randomly, which is the effective strategy when the system does not have any assumption or knowledge of the environment. We also investigated sensory blind prediction which should allow action plannning as well as offer a reliable forecast for a safe evolution of the robot in the environment. The simulation and experimental results show that the system learns interaction between the robot and the environment in high fidelity.