Towards a data-driven approach to human preferences in motion planning

Co-robots, i.e. robots that work close to people, will need to account for the preferences and expectations of their human co-workers in executing trajectories or actions. Consistent, legible and predictable trajectories are a key factor in making humans comfortable around robots. In this work, we take a data-driven approach towards designing robot trajectories that are more acceptable to human co-workers and observers. We use an online survey to ask people to rate multiple robot trajectories generated in a variety of environments. We compute a large set of features for each trajectory, also taking into account environment information. We use a combination of the features and the survey ratings to learn a classifier that predicts the rating for a new trajectory based on the learned human-observer preferences. The classifier also helps identify and highlight the most important features that influence people's ratings of the trajectories. Finally, we discuss how a data-driven approach using the results of this analysis can be used to help design better trajectories that are more acceptable to people.