Learning to detect and adapt to unpredicted changes

To survive in the real world, a robot must be able to intelligently react to unpredicted and possibly simultaneous changes to its self (such as its sensors, actions, and goals) and dynamic situations/configurations in the environment. Typically there is a great deal of human knowledge required to transfer essential control details to the robot, which precisely describe how to operate its actuators based on environmental conditions detected by sensors. Despite the best preventative efforts, unpredicted changes such as hardware failure are unavoidable. Hence, an autonomous robot must detect and adapt to unpredicted changes in an unsupervised manner.;This dissertation presents an integrated technique called Surprise-Based Learning (SBL) to address this challenge. The main idea is to have a robot perform both learning and representation in parallel by constructing and maintaining a predictive model which explains the interactions between the robot and the environment. A robot using SBL engages in a life-long cyclic learning process consisting of "prediction, action, observation, analysis (of surprise) and adaptation". In particular, the robot always predicts the consequences of its actions, detects surprises whenever there is a significant discrepancy between the prediction and observed reality, analyzes the surprises for its causes (correlations) and uses critical knowledge extracted from the analysis to adapt itself to unpredicted situations.;SBL provides four new contributions to robotic learning. The first contribution is a novel method for structure learning capable of learning accurate enough models of interactions in an environment in an unsupervised manner. The second contribution is learning directly from uninterpreted sensors and actions with the aid of a few comparison operators. The third contribution is detecting and adapting to simultaneous unpredicted changes in sensors, actions, goals and the environment. The fourth contribution is detecting and reasoning with unpredicted interference over a short period of time.;Experiments on both simulation and real robots have shown that SBL can learn accurate models of interactions and successfully adapt to unpredicted changes in the robot's actions, sensors, goals and the environment's configuration while navigating in different environments. Experiments on surveillance videos have shown that SBL can detect interference, and recover some information that was hidden from sensors, in the presence of noise and gaps in the data stream.