In this work, we formulate the process of generating explanations as model reconciliation for planning problems as one of planning with explanatory actions. We show that these problems could be better understood within the framework of epistemic planning and that, in fact, most earlier works on explanation as model reconciliation correspond to tractable subsets of epistemic planning problems. We empirically show how our approach is computationally more efficient than existing techniques for explanation generation and we end the paper with a discussion of how this formulation could be extended to generate novel explanatory behaviors.
Our work also looks at the use of explanatory actions as a means of communicating information to the human observer. The most obvious types of such explanatory action includes purely communicative actions such as speech @cite_5 or the use of mixed reality projections @cite_18 @cite_4 , but recent works have shown that physical agents could also use movements to relay information such as intention @cite_6 @cite_17 and incapability @cite_22 . Our framework could be easily adopted to any of these explanatory actions and would naturally allow for a trade-off between these different types of communication.
@cite_18 @cite_4 @cite_22 @cite_6 @cite_5 @cite_17
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Recent advances in mixed-reality technologies have renewed interest in alternative modes of communication for human-robot interaction. However, most of the work in this direction has been confined to tasks such as teleoperation, simulation or explication of individual actions of a robot. In this paper, we will discuss how the capability to project intentions affect the task planning capabilities of a robot. Specifically, we will start with a discussion on how projection actions can be used to reveal information regarding the future intentions of the robot at the time of task execution. We will then pose a new planning paradigm - projection-aware planning - whereby a robot can trade off its plan cost with its ability to reveal its intentions using its projection actions. We will demonstrate each of these scenarios with the help of a joint human-robot activity using the HoloLens. Our goal is to enable robots to express their incapability, and to do so in a way that communicates both what they are trying to accomplish and why they are unable to accomplish it. We frame this as a trajectory optimization problem: maximize the similarity between the motion expressing incapability and what would amount to successful task execution, while obeying the physical limits of the robot. We introduce and evaluate candidate similarity measures, and show that one in particular generalizes to a range of tasks, while producing expressive motions that are tailored to each task. Our user study supports that our approach automatically generates motions expressing incapability that communicate both what and why to end-users, and improve their overall perception of the robot and willingness to collaborate with it in the future. We introduce a novel framework to formalize and solve transparent planning tasks by executing actions selected in a suitable and timely fashion. A transparent planning task is defined as a task where the objective of the agent is to communicate its true goal to observers, thereby making its intentions and its action selection transparent. We formally define and model these tasks as Goal POMDPs where the state space is the Cartesian product of the states of the world and a given set of hypothetical goals. Action effects are deterministic in the world states of the problem but probabilistic in the observer's beliefs. Transition probabilities are obtained from making a call to a model-based plan recognition algorithm, which we refer to as an observer stereotype. We propose an action selection strategy via on-line planning that seeks actions to quickly convey the goal being pursued to an observer assumed to fit a given stereotype. In order to keep run-times feasible, we propose a novel model-based plan recognition algorithm that approximates well-known probabilistic plan recognition methods. The resulting on-line planner, after being evaluated over a diverse set of domains and three different observer stereotypes, is found to convey goal information faster than purely goal-directed planners. A key requirement for seamless human-robot collaboration is for the robot to make its intentions clear to its human collaborator. A collaborative robot's motion must be legible, or intent-expressive. Legibility is often described in the literature as and effect of predictable, unsurprising, or expected motion. Our central insight is that predictability and legibility are fundamentally different and often contradictory properties of motion. We develop a formalism to mathematically define and distinguish predictability and legibility of motion. We formalize the two based on inferences between trajectories and goals in opposing directions, drawing the analogy to action interpretation in psychology. We then propose mathematical models for these inferences based on optimizing cost, drawing the analogy to the principle of rational action. Our experiments validate our formalism's prediction that predictability and legibility can contradict, and provide support for our models. Our findings indicate that for robots to seamlessly collaborate with humans, they must change the way they plan their motion.