%0 Journal Article
%T PlanIt: A Crowdsourcing Approach for Learning to Plan Paths from Large Scale Preference Feedback
%A Ashesh Jain
%A Debarghya Das
%A Ashutosh Saxena
%J Computer Science
%D 2014
%I arXiv
%X We consider the problem of learning user preferences over robot trajectories in environments rich in objects and humans. This is challenging because the criterion defining a good trajectory varies with users, tasks and interactions in the environments. We use a cost function to represent how preferred the trajectory is; the robot uses this cost function to generate a trajectory in a new environment. In order to learn this cost function, we design a system - PlanIt, where non-expert users can see robots motion for different asks and label segments of the video as good/bad/neutral. Using this weak, noisy labels, we learn the parameters of our model. Our model is a generative one, where the preferences are expressed as function of grounded object affordances. We test our approach on 112 different environments, and our extensive experiments show that we can learn meaningful preferences in the form of grounded planning affordances, and then use them to generate preferred trajectories in human environments.
%U http://arxiv.org/abs/1406.2616v2