Decision-Theoretic Planning with Person Trajectory Prediction for Social Navigation

Robots navigating in a social way should reason about people intentions when acting. For instance, in applications like robot guidance or meeting with a person, the robot has to consider the goals of the people. Intentions are inherently nonobservable, and thus we propose Partially Observable Markov Decision Processes (POMDPs) as a decision-making tool for these applications. One of the issues with POMDPs is that the prediction models are usually handcrafted. In this paper, we use machine learning techniques to build prediction models from observations. A novel technique is employed to discover points of interest (goals) in the environment, and a variant of Growing Hidden Markov Models (GHMMs) is used to learn the transition probabilities of the POMDP. The approach is applied to an autonomous telepresence robot

Developing an Instrument to Examine Preservice Teachers' Pedagogical Development

National and international reform documents have forged blueprints for advancing science education. Coursework for preservice teachers needs to correspond to these documents by providing learning experiences that develop preservice teachers' capabilities to plan and implement reform measures. Using a pretest–posttest design, responses from 59 2nd-year preservice teachers from the same university were compared after involvement in an elementary science pedagogy coursework. The survey, which was linked to the course outcomes (constructs) and multiple indicators, measured the preservice teachers' perceptions of their development towards becoming elementary science teachers. A pretest–posttest survey linked to course outcomes can be employed to assess perceived pedagogical development of preservice teachers, which can inform further teaching practices for implementing science education reform agendas