By Arvin Agah and John-John Cabibihan

This booklet constitutes the refereed court cases of the eighth overseas convention on Social Robotics, ICSR 2016, held in Kansas urban, MO, united states, in November 2016. The ninety eight revised complete papers awarded have been conscientiously reviewed and chosen from 107 submissions.

The subject matter of the 2016 convention is Sociorobotics: layout and implementation of social behaviors of robots interacting with one another and people. as well as technical periods, ICSR 2016 integrated 3 workshops: the artificial approach in Social Robotics (SMSR 2016), Social Robots: a device to improve Interventions for Autism, and utilizing Social Robots to enhance the standard of existence within the aged.

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Designing a socially assistive robot for personalized number concepts learning in preschool children. In: International Conference on Development and Learning and Epigenetic Robotics, pp. 314–319 (2015) 6. : Dimensionality reduction for probabilistic movement primitives. In: IEEE-RAS International Conference on Humanoid Robots, pp. 794–800 (2014) 7. : External force estimation during compliant robot manipulation. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3535–3540 (2013) 8.

Human input can be either in the form of feedback or guidance. Learning from Feedback treats the human input as a reinforcement signal after the executed action [14]. Learning from Guidance allows human intervention to the selected action before execution, proposing (corrective) actions [8]. To our knowledge, IRL methods have not been investigated for the adaptation of an agent to a new environment. Hence, we propose their integration to the adaptation mechanism, as policy evaluation metrics used to evaluate and modify a learned policy towards an optimal one, following proper transfer methods.

Particularly, the approach makes use of Gaussian Mixture Models (GMMs) to model the physical interaction of the robot and the person when the robot is teleoperated or guided by an expert. The learned models are integrated into a sample-based planner, an RRT*, at two levels: as a cost function in order to plan trajectories considering behavior constraints, and as configuration space sampling bias to discard samples with low cost according to the behaviors. The algorithm is successfully tested in the laboratory using an actual robot and real trajectories examples provided by an expert.

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