Coordinating Human-Robot Teams with Dynamic and Stochastic Task Proficiencies

As robots become ubiquitous in the workforce, it is essential that human-robot collaboration be both intuitive and adaptive. A robot’s ability to coordinate team activities improves based on its ability to infer and reason about the dynamic (i.e., the “learning curve”) and stochastic task performance of its human counterparts. We introduce a novel resource coordination algorithm that enables robots to schedule team activities by (1) actively characterizing the task performance of their human teammates and (2) ensuring the schedule is robust to temporal constraints given this characterization. We first validate our modeling assumptions via user study. From this user study, we create a data-driven prior distribution over human task performance for our virtual and physical evaluations of human-robot teaming. Second, we show that our methods are scalable and produce high-quality schedules. Third, we conduct a between-subjects experiment (n = 90) to assess the effects on a human-robot team of a robot scheduler actively exploring the humans’ task proficiency. Our results indicate that human-robot working alliance ( p\lt 0.001 ) and human performance ( p=0.00359 ) are maximized when the robot dedicates more time to exploring the capabilities of human teammates.

A partition-based branch-and-bound algorithm for the project duration problem with partially renewable resources and general temporal constraints

The concept of partially renewable resources provides a general modeling framework that can be used for a wide range of different real-life applications. In this paper, we consider a resource-constrained project duration problem with partially renewable resources, where the temporal constraints between the activities are given by minimum and maximum time lags. We present a new branch-and-bound algorithm for this problem, which is based on a stepwise decomposition of the possible resource consumptions by the activities of the project. It is shown that the new approach results in a polynomially bounded depth of the enumeration tree, which is obtained by kind of a binary search. In a comprehensive experimental performance analysis, we compare our exact solution procedure with all branch-and-bound algorithms and state-of-the-art heuristics from the literature on different benchmark sets. The results of the performance study reveal that our branch-and-bound algorithm clearly outperforms all exact solution procedures. Furthermore, it is shown that our new approach dominates the state-of-the-art heuristics on well known benchmark instances.

Navigating Shomoyscapes: Time and faculty life in the urban Global South

We aim to decenter the Global North knowledge production about time in higher education (HE) by introducing and applying a culturally sustaining concept of shomoyscapes. While the Bengali word “shomoy” literally means “time,” it goes beyond “clock time” and also refers to memories, present moments, feelings, a particular duration, and/or signifier for a temporal engagement. A shomoyscape entails a complex temporal landscape of different temporal categories, constraints, agencies, and to various degrees, embodies hybrid times (i.e., modern time coexisting with non-linear local/traditional time). Drawing on interviews and participant observations with 22 faculty in Dhaka, Bangladesh, we demonstrate the efficacy of shomoyscapes by illuminating how faculty experience, contest, and manipulate their time(s) amid rapid socio-economic transformations of Dhaka, an urban, Global South mega city. We show how shomoyscapes manifest as faculty experience temporal constraints, such as (a) traffic, (b) party-based university politics, and (c) caring for others. We suggest that Bangladeshi faculty experience and navigate shomoyscapes that are constituted by both larger temporal constraints (spatial, structural, or relational) and their temporal agency in response to these same constraints. Using a temporal lens, we contribute to a more in depth understanding of the experiences of faculty working and living in an urban, Global South context, highlighting how life “outside the academy” spills over into working “inside the academy,” rather than vice versa. We argue that shomoyscapes offer a useful temporal heuristic to help contextualize human/social relations in different arenas of social life that would otherwise remain invisible.

Spatial and Temporal Constraints on the Composition of Microbial Communities in Subsurface Boreholes of the Edgar Experimental Mine

The Edgar Experimental Mine, Idaho Springs, CO, provides inexpensive and open access to borehole investigations for subsurface microbiology studies. Understanding how microbial processes in the near subsurface are connected to surface hydrological influences is lacking.

Similarity Matrix-Based Anomaly Detection for Clinical Intervention

The use of digital phenotyping methods in clinical care has allowed for improved investigation of spatiotemporal behaviors of patients. Moreover, detecting abnormalities in mobile sensor data patterns can be instrumental in identifying potential changes in symptomology. We propose a method that temporally aligns sensor data in order to achieve interpretable measures of similarity. These computed measures can then be used for anomaly detection, baseline routine computation, and trajectory clustering. In addition, we apply this method on a study of 695 college participants, as well as on a patient with worsening anxiety and depression. With varying temporal constraints, we find mild correlations between changes in routine and clinical scores. Furthermore, in our experiment on an individual with elevated depression and anxiety, we are able to cluster GPS trajectories, allowing for improved understanding and visualization of routines with respect to symptomology. In the future, we aim to apply this method on individuals that undergo data collection for longer periods of time, thus allowing for a better understanding of long-term routines and signals for clinical intervention.