We propose an abstraction method for open-world environments expressed as Factored Markov Decision Processes (FMDPs) with very large state and action spaces. Our method prunes state and action variables that are irrelevant to the optimal value function on the state subspace the agent would visit when following any optimal policy from the initial state. This method thus enables tractable fast planning within large open-world FMDPs.

We present a decision-theoretic model and robot system that interprets multimodal human communication to disambiguate item references by asking questions via a mixed reality (MR) interface. Existing approaches have either chosen to use physical behaviors, like pointing and eye gaze, or virtual behaviors, like mixed reality. However, there is a gap of research on how MR compares to physical actions for reducing robot uncertainty. We test the hypothesis that virtual deictic gestures are better for human-robot interaction (HRI) than physical behaviors. To test this hypothesis, we propose the Physio-Virtual Deixis Partially Observable Markov Decision Process (PVD-POMDP), which interprets multimodal observations (speech, eye gaze, and pointing gestures) from the human and decides when and how to ask questions (either via physical or virtual deictic gestures) in order to recover from failure states and cope with sensor noise. We conducted a between-subjects user study with 80 participants distributed across three conditions of robot communication: no feedback control, physical feedback, and MR feedback. We tested performance of each condition with objective measures (accuracy, time), as well as evaluated user experience with subjective measures (usability, trust, workload). We found the MR feedback condition was 10% more accurate than the physical condition and a speedup of 160%. We also found that the feedback conditions significantly outperformed the no feedback condition in all subjective metrics.

A core capability of robots is to reason about multiple objects under uncertainty. Partially Observable Markov Decision Processes (POMDPs) provide a means of reasoning under uncertainty for sequential decision making, but are computationally intractable in large domains. In this paper, we propose Object-Oriented POMDPs (OO-POMDPs), which represent the state and observation spaces in terms of classes and objects. The structure afforded by OO-POMDPs support a factorization of the agent's belief into independent object distributions, which enables the size of the belief to scale linearly versus exponentially in the number of objects. We formulate a novel Multi-Object Search (MOS) task as an OO-POMDP for mobile robotics domains in which the agent must find the locations of multiple objects. Our solution exploits the structure of OO-POMDPs by featuring human language to selectively update the belief at task onset. Using this structure, we develop a new algorithm for efficiently solving OO-POMDPs: Object-Oriented Partially Observable Monte-Carlo Planning (OOPOMCP). We show that OO-POMCP with grounded language commands is sufficient for solving challenging MOS tasks both in simulation and on a physical mobile robot.