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In this paper, we consider the problem of designing collision-free,dynamically feasible, and socially-aware trajectories for robots operating inenvironments populated by humans. We define trajectories to be social-aware ifthey do not interfere with humans in any way that causes discomfort. In thispaper, discomfort is defined broadly and, depending on specific individuals, itcan result from the robot being too close to a human or from interfering withhuman sight or tasks. Moreover, we assume that human feedback is a banditfeedback indicating a complaint or no complaint on the part of the robottrajectory that interferes with the humans, and it does not reveal anycontextual information about the locations of the humans or the reason for acomplaint. Finally, we assume that humans can move in the obstacle-free spaceand, as a result, human utility can change. We formulate this planning problemas an online optimization problem that minimizes the social value of thetime-varying robot trajectory, defined by the total number of incurred humancomplaints. As the human utility is unknown, we employ zeroth order, orderivative-free, optimization methods to solve this problem, which we combinewith off-the-shelf motion planners to satisfy the dynamic feasibility andcollision-free specifications of the resulting trajectories. To the best of ourknowledge, this is a new framework for socially-aware robot planning that isnot restricted to avoiding collisions with humans but, instead, focuses onincreasing the social value of the robot trajectories using only bandit humanfeedback.

Socially-Aware Robot Planning via Bandit Human Feedback