  Creating robots that can perform general-purpose service tasks in a
human-populated environment has been a longstanding grand challenge for AI and
Robotics research. One particularly valuable skill that is relevant to a wide
variety of tasks is the ability to locate and retrieve objects upon request.
This paper models this skill as a Scavenger Hunt (SH) game, which we formulate
as a variation of the NP-hard stochastic traveling purchaser problem. In this
problem, the goal is to find a set of objects as quickly as possible, given
probability distributions of where they may be found. We investigate the
performance of several solution algorithms for the SH problem, both in
simulation and on a real mobile robot. We use Reinforcement Learning (RL) to
train an agent to plan a minimal cost path, and show that the RL agent can
outperform a range of heuristic algorithms, achieving near optimal performance.
In order to stimulate research on this problem, we introduce a publicly
available software stack and associated website that enable users to upload
scavenger hunts which robots can download, perform, and learn from to
continually improve their performance on future hunts.
