Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Shape Classification and Analysis: Theory and Practice
Shape Classification and Analysis: Theory and Practice
Robot Programming by Demonstration
Robot Programming by Demonstration
Autonomous Helicopter Aerobatics through Apprenticeship Learning
International Journal of Robotics Research
Incremental Learning of Tasks From User Demonstrations, Past Experiences, and Vocal Comments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning in behavior-based multi-robot systems: policies, models, and other agents
Cognitive Systems Research
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This paper proposes a new robot learning framework to acquire scenario specific autonomous behaviors by demonstration. We extract visual features from the demonstrated behavior examples in an indoor environment and transfer it onto an underlying set of scenario aware robot behaviors. Demonstrations are performed using an omnidirectional camera as training instances in different indoor scenarios are registered.The features that distinguish the environment are identified and are used to classify the traversing scenarios. Once the scenario is identified, a behavior model trained by means of artificial neural network pertaining to the specific scenario is learned. The generalization ability of the behavior model is evaluated for seen and unseen data. As a comparison, the behaviors attained using a monolithic general purpose model and its generalization ability against the former is evaluated. The experimental results on the mobile robot indicate the acquired behavior is robust and generalizes meaningful actions beyond the specifics presented during training.