We address problems underlying the algorithmic question of automating the co-design of robot hardware in tandem with its apposite software. Specifically, we consider the impact that degradations of a robot’s sensor and actuation suites may have on the ability of that robot to complete its tasks. We introduce a new formal structure that generalizes and consolidates a variety of well-known structures including many forms of plans, planning problems, and filters, into a single data structure called a procrustean graph, and give these graph structures semantics in terms of ideas based in formal language theory. We describe a collection of operations on procrustean graphs (both semantics-preserving and semantics-mutating), and show how a family of questions about the destructiveness of a change to the robot hardware can be answered by applying these operations. We also highlight the connections between this new approach and existing threads of research, including combinatorial filtering, Erdmann’s strategy complexes, and hybrid automata.

This paper addresses a navigation problem for a certain type of simple mobile robot modeled as a point moving in the plane. The only requirement on the robot is that it must be able to translate in a desired direction, with bounded angular error (measured in a global reference frame), until it reaches the nearest obstacle in its motion direction. One straightforward realization of this capability might use a noisy compass and a contact sensor. We present a planning algorithm that enables such a robot to navigate reliably through its environment. The algorithm constructs a directed graph in which each node is labeled with a subset of the environment boundary. Each edge of the graph is labeled with a sequence of actions that can move the robot from any location in one such set to some location in the other set. We use a variety of local planners to generate the edges, including a “corner-finding” technique that allows the robot to travel to and localize itself at a convex vertex of the environment boundary. The algorithm forms complete plans by searching the resulting graph. We have implemented this algorithm and present results from both simulation and a proof-of-concept physical realization.

Background: Medication nonadherence is a global public health challenge that results in suboptimal health outcomes and increases health care costs. Forgetting to take medicines is one of the most common reasons for unintentional medication nonadherence. Research findings indicate that voice-activated virtual home assistants, such as Amazon Echo and Google Home devices, may be useful in promoting medication adherence. Objective: This study aims to create a medication adherence app (skill), MedBuddy, for Amazon Echo devices and measure the use, usability, and usefulness of this medication-taking reminder skill. Methods: A single-group, mixed methods, cohort feasibility study was conducted with women who took oral contraceptives (N=25). Participants were undergraduate students (age: mean 21.8 years, SD 6.2) at an urban university in the Southeast United States. Participants were given an Amazon Echo Dot with MedBuddy—a new medication reminder skill for Echo devices created by our team—attached to their study account, which they used for 60 days. Participants self-reported their baseline and poststudy medication adherence. MedBuddy use was objectively evaluated by tracking participants’ interactions with MedBuddy through Amazon Alexa. The usability and usefulness of MedBuddy were evaluated through a poststudy interview in which participants responded to both quantitative and qualitative questions. Results: Participants’ interactions with MedBuddy, as tracked through Amazon Alexa, only occurred on half of the study days (mean 50.97, SD 29.5). At study end, participants reported missing their medication less in the past 1 and 6 months compared with baseline (χ21=0.9 and χ21=0.4, respectively; McNemar test: P<.001 for both). However, there was no significant difference in participants’ reported adherence to consistently taking medication within the same 2-hour time frame every day in the past 1 or 6 months at the end of the study compared with baseline (χ21=3.5 and χ21=0.4, respectively; McNemar test: P=.63 and P=.07, respectively). Overall feedback about usability was positive, and participants provided constructive feedback about the skill’s features that could be improved. Participants’ evaluation of MedBuddy’s usefulness was overwhelmingly positive—most (15/23, 65%) said that they would continue using MedBuddy as a medication reminder if provided with the opportunity and that they would recommend it to others. MedBuddy features that participants enjoyed were an external prompt separate from their phone, the ability to hear the reminder prompt from a separate room, multiple reminders, and verbal responses to prompts. Conclusions: The findings of this feasibility study indicate that the MedBuddy medication reminder skill may be useful in promoting medication adherence. However, the skill could benefit from further usability enhancements.

Robotic systems have subsystems with a combinatorially large configuration space and hundreds or thousands of possible software and hardware configuration options interacting non-trivially. The configurable parameters are set to target specific objectives, but they can cause functional faults when incorrectly configured. Finding the root cause of such faults is challenging due to the exponentially large configuration space and the dependencies between the robot's configuration settings and performance. This paper proposes C a RE—a method for diagnosing the root cause of functional faults through the lens of causality. C a RE abstracts the causal relationships between various configuration options and the robot's performance objectives by learning a causal structure and estimating the causal effects of options on robot performance indicators. We demonstrate C a RE’s efficacy by finding the root cause of the observed functional faults and validating the diagnosed root cause by conducting experiments in both physical robots ( Husky and Turtlebot 3 ) and in simulation ( Gazebo ). Furthermore, we demonstrate that the causal models learned from robots in simulation (e.g., Husky in Gazebo ) are transferable to physical robots across different platforms (e.g., Husky and Turtlebot 3 ).

This article examines the selection of a robot’s actuation and sensing hardware to minimize the cost of that design while ensuring that the robot is capable of carrying out a plan to complete a task. Its primary contribution is in the study of the hardness of reasonable formal models for that minimization problem. Specifically, for the case in which sensing hardware is held fixed, we show that this algorithmic design problem is NP-hard even for particularly simple classes of cost functions, confirming what many perhaps have suspected about this sort of design-time optimization. We also introduce a formalism, based on the notion of label maps, for the broader problem in which the design space encompasses choices for both actuation and sensing components. As a result, for several questions of interest, having both optimality and efficiency of solution is unlikely. However, we also show that, for some specific types of cost functions, the problem is either polynomial-time solvable or fixed-parameter tractable. Note to Practitioners —Despite the primary results being theoretical and, further, taking the form of bad news, this article still has considerable value to practitioners. Specifically, assuming that one has been employing heuristic or approximate solutions to robot design problems, this article serves as a justification for doing so. Moreover, it delineates some circumstances in which one can, in a sense, do better and achieve genuine optima with practical algorithms.