Advanced Robotics: Redundancy and Optimization
Advanced Robotics: Redundancy and Optimization
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A hybrid architecture for adaptive robot control
A hybrid architecture for adaptive robot control
Nonverbal leakage in robots: communication of intentions through seemingly unintentional behavior
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
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This paper advocates an approach for learning communicative actions and manual skills in the same framework. We exploit a fundamental relationship between the structure of motor skills, intention, and communication. Communicative actions are acquired using the same learning framework and the same primitive states and actions that the robot uses to construct manual behavior for interacting with other objects in the environment. A prospective behavior algorithm is used to acquire modular policies for conveying intention and goals to nearby human beings and recruiting their assistance. The learning framework and a preliminary case study are presented in which a humanoid robot learns expressive communicative behavior incrementally by discovering the manual affordances of human beings. Results from interactions with 16 people provide support for the hypothesized benefits of this approach. Behavior reuse makes learning from relatively few interactions possible. This approach compliments other efforts in the field by grounding social behavior, and proposes a mechanism for negotiating a communicative vocabulary between humans and robots.