  In this paper, we consider a robot navigation problem in environments
populated by humans. The goal is to determine collision-free and dynamically
feasible trajectories that also maximize human satisfaction. This is because
they may drive the robot close to humans that need help with their work or
because they may keep the robot away from humans when it can interfere with
human sight or work. In practice, human satisfaction is subjective and hard to
describe mathematically. As a result, the planning problem we consider in this
paper may lack important contextual information. To address this challenge, we
propose a semi-supervised Bayesian Optimization (BO) method to design globally
optimal robot trajectories using non-contextual bandit human feedback in the
form of complaints or satisfaction ratings that express how satisfactory a
trajectory is, without revealing the reason. Since trajectory planning is
typically a high-dimensional optimization problem in the space of waypoints
that define a trajectory, BO may require prohibitively many queries for human
feedback to return a good solution. To this end, we use an autoencoder to
reduce the high-dimensional problem space into a low dimensional latent space,
which we update using human feedback. Moreover, we improve the exploration
efficiency of BO by biasing the search for new trajectories towards dynamically
feasible and collision-free trajectories obtained using off-the-shelf motion
planners. We demonstrate the efficiency of our proposed trajectory planning
method in a scenario with humans that have diversified and unknown demands.
