We consider the problem of learning user preferences over robot trajectories for environments rich in objects and humans. This is challenging because the criterion defining a good trajectory varies with users, tasks and interactions in the environment. We represent trajectory preferences using a cost function that the robot learns and uses it to generate good trajectories in new environments. We design a crowdsourcing system -PlanIt, where non-expert users label segments of the robot's trajectory. PlanIt allows us to collect a large amount of user feedback, and using the weak and noisy labels from PlanIt we learn the parameters of our model. We test our approach on 122 different environments for robotic navigation and manipulation tasks. Our extensive experiments show that the learned cost function generates preferred trajectories in human environments. Our crowdsourcing system is publicly available for the visualization of the learned costs and for providing preference feedback: http://planit.cs.cornell.edu

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This paper presents an articulatory biofeedback system and discusses new research methods made possible by this technology. The real-time electromagnetic articulography biofeedback system (RT-EMA) enables speakers to observe a visual representation of the movements of their speech articulators while they are speaking. Investigators can dynamically control the visual display of virtual targets or other objects in vocal tract space, track events involving interactions between virtual objects and articulators, and define custom actions in response to such events. Preliminary findings from experimental studies and games employing biofeedback are reported, with emphasis on the potential applications of articulatory biofeedback for investigating questions of linguistic interest.