Human to robot demonstrations of routine home tasks: exploring the role of the robot's feedback
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Incremental Learning and Memory Consolidation of Whole Body Human Motion Primitives
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A model of cooperative agent based on imitation and Maslow's pyramid of needs
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Induced states in a decision tree constructed by Q-learning
Information Sciences: an International Journal
Common sensorimotor representation for self-initiated imitation learning
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Behaviour generation in humanoids by learning potential-based policies from constrained motion
Applied Bionics and Biomechanics
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This paper addresses the problem of body mapping in robotic imitation where the demonstrator and imitator may not share the same embodiment [degrees of freedom (DOFs), body morphology, constraints, affordances, and so on]. Body mappings are formalized using a unified (linear) approach via correspondence matrices, which allow one to capture partial, mirror symmetric, one-to-one, one-to-many, many-to-one, and many-to-many associations between various DOFs across dissimilar embodiments. We show how metrics for matching state and action aspects of behavior can be mathematically determined by such correspondence mappings, which may serve to guide a robotic imitator. The approach is illustrated and validated in a number of simulated 3-D robotic examples, using agents described by simple kinematic models and different types of correspondence mappings