PhD Thesis Proposal: Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Resource optimization in health care, manufacturing, and military operations requires the careful choreography of people and equipment to effectively fulfill the responsibilities of the profession. However, resource optimization is a computationally challenging problem, and poorly utilizing resources can have drastic consequences. Within these professions, there are human domain experts who are able to learn from experience to develop strategies, heuristics, and rules-of-thumb to effectively utilize the resources at their disposal. Manually codifying these heuristics within a computational tool is a laborious process and leaves much to be desired. Even with a codified set of heuristics, it is not clear how to best insert an autonomous decision-support system into the human decision-making process. The aim of this thesis is to develop an autonomous computational method for learning domain-expert heuristics from demonstration that can support the human decision-making process. We propose a new framework, called apprenticeship scheduling, which learns and embeds these heuristics within a scalable resource optimization algorithm for real-time decision-support. Our initial investigation, comprised of developing scalable methods for scheduling and studying shared control in human-machine collaborative resource optimization, inspires the development of our apprenticeship scheduling approach. We present a promising, initial prototype for learning heuristics from demonstration and outline a plan for our continuing work

Fast methods for scheduling with applications to real-time systems and large-scale, robotic manufacturing of aerospace structures

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 113-117).Across the aerospace and automotive manufacturing industries, there is a push to remove the cage around large, industrial robots and integrate right-sized, safe versions into the human labor force. By integrating robots into the labor force, humans can be freed to focus on value-added tasks (e.g. dexterous assembly) while the robots perform the non-value-added tasks (e.g. fetching parts). For this integration to be successful, the robots need to ability to reschedule their tasks online in response to unanticipated changes in the parameters of the manufacturing process. The problem of task allocation and scheduling is NP-Hard. To achieve good scalability characteristics, prior approaches to autonomous task allocation and scheduling use decomposition and distributed techniques. These methods work well for domains such as UAV scheduling when the temporospatial constraints can be decoupled or when low network bandwidth makes inter-agent communication difficult. However, the advantages of these methods are mitigated in the factory setting where the temporospatial constraints are tightly inter-coupled from the humans and robots working in close proximity and where there is sufficient network bandwidth. In this thesis, I present a system, called Tercio, that solves large-scale scheduling problems by combining mixed-integer linear programming to perform the agent allocation and a real-time scheduling simulation to sequence the task set. Tercio generates near optimal schedules for 10 agents and 500 work packages in less than 20 seconds on average and has been demonstrated in a multi-robot hardware test bed. My primary technical contributions are fast, near-optimal, real-time systems methods for scheduling and testing the schedulability of task sets. I also present a pilot study that investigates what level of control the Tercio should give human workers over their robotic teammates to maximize system efficiency and human satisfaction.by Matthew C. Gombolay.S.M

Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints

The application of robotics to traditionally manual manufacturing processes requires careful coordination between human and robotic agents in order to support safe and efficient coordinated work. Tasks must be allocated to agents and sequenced according to temporal and spatial constraints. Also, systems must be capable of responding on-the-fly to disturbances and people working in close physical proximity to robots. In this paper, we present a centralized algorithm, named 'Tercio,' that handles tightly intercoupled temporal and spatial constraints. Our key innovation is a fast, satisficing multi-agent task sequencer inspired by real-time processor scheduling techniques and adapted to leverage a hierarchical problem structure. We use this sequencer in conjunction with a mixed-integer linear program solver and empirically demonstrate the ability to generate near-optimal schedules for real-world problems an order of magnitude larger than those reported in prior art. Finally, we demonstrate the use of our algorithm in a multirobot hardware testbed

MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving

Personalization of autonomous vehicles (AV) may significantly increase trust, use, and acceptance. In particular, we hypothesize that the similarity of an AV's driving style compared to the end-user's driving style will have a major impact on end-user's willingness to use the AV. To investigate the impact of driving style on user acceptance, we 1) develop a data-driven approach to personalize driving style and 2) demonstrate that personalization significantly impacts attitudes towards AVs. Our approach learns a high-level model that tunes low-level controllers to ensure safe and personalized control of the AV. The key to our approach is learning an informative, personalized embedding that represents a user's driving style. Our framework is capable of calibrating the level of aggression so as to optimize driving style based upon driver preference. Across two human subject studies (n = 54), we first demonstrate our approach mimics the driving styles of end-users and can tune attributes of style (e.g., aggressiveness). Second, we investigate the factors (e.g., trust, personality etc.) that impact homophily, i.e. an individual's preference for a driving style similar to their own. We find that our approach generates driving styles consistent with end-user styles (p<.001) and participants rate our approach as more similar to their level of aggressiveness (p=.002). We find that personality (p<.001), perceived similarity (p<.001), and high-velocity driving style (p=.0031) significantly modulate the effect of homophily

Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment

We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent. The heterogeneous search team may only have access to a limited number of past adversary trajectories within a large search space. This problem is challenging for both model-based searching and reinforcement learning (RL) methods since the adversary exhibits reactionary and deceptive evasive behaviors in a large space leading to sparse detections for the search agents. To address this challenge, we propose a novel Multi-Agent RL (MARL) framework that leverages the estimated adversary location from our learnable filtering model. We show that our MARL architecture can outperform all baselines and achieves a 46% increase in detection rate.Comment: Accepted by IEEE International Symposium on Multi-Robot & Multi-Agent Systems (MRS) 202

Learning Models of Adversarial Agent Behavior under Partial Observability

The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction. In this work, we present Graph based Adversarial Modeling with Mutal Information (GrAMMI) for modeling the behavior of an adversarial opponent agent. GrAMMI is a novel graph neural network (GNN) based approach that uses mutual information maximization as an auxiliary objective to predict the current and future states of an adversarial opponent with partial observability. To evaluate GrAMMI, we design two large-scale, pursuit-evasion domains inspired by real-world scenarios, where a team of heterogeneous agents is tasked with tracking and interdicting a single adversarial agent, and the adversarial agent must evade detection while achieving its own objectives. With the mutual information formulation, GrAMMI outperforms all baselines in both domains and achieves 31.68% higher log-likelihood on average for future adversarial state predictions across both domains.Comment: 8 pages, 3 figures, 2 table

Decision-Making Authority, Team Efficiency and Human Worker Satisfaction in Mixed Human-Robot Teams

has opened up the possibility of integrating highly autonomous mobile robots into human teams. However, with this capability comes the issue of how to maximize both team efficiency and the desire of human team members to work with robotic counterparts. We hypothesized that giving workers partial decision-making authority over a task allocation process for the scheduling of work would achieve such a maximization, and conducted an experiment on human subjects to test this hypothesis. We found that an autonomous robot can outperform a worker in the execution of part or all of the task allocation (p &lt; 0.001 for both). However, rather than finding an ideal balance of control authority to maximize worker satisfaction, we observed that workers preferred to give control authority to the robot (p &lt; 0.001). Our results indicate that workers prefer to be part of an efficient team rather than have a role in the scheduling process, if maintaining such a role decreases their efficiency. These results provide guidance for the successful introduction of semi-autonomous robots into human teams. I

Robotic Assistance in Coordination of Patient Care

We conducted a study to investigate trust in and dependence upon robotic decision support among nurses and doctors on a labor and delivery floor. There is evidence that suggestions provided by embodied agents engender inappropriate degrees of trust and reliance among humans. This concern is a critical barrier that must be addressed before fielding intelligent hospital service robots that take initiative to coordinate patient care. Our experiment was conducted with nurses and physicians, and evaluated the subjects’ levels of trust in and dependence on high- and low-quality recommendations issued by robotic versus computer-based decision support. The support, generated through action-driven learning from expert demonstration, was shown to produce high-quality recommendations that were ac- cepted by nurses and physicians at a compliance rate of 90%. Rates of Type I and Type II errors were comparable between robotic and computer-based decision support. Furthermore, em- bodiment appeared to benefit performance, as indicated by a higher degree of appropriate dependence after the quality of recommendations changed over the course of the experiment. These results support the notion that a robotic assistant may be able to safely and effectively assist in patient care. Finally, we conducted a pilot demonstration in which a robot assisted resource nurses on a labor and delivery floor at a tertiary care center.National Science Foundation (U.S.) (Grant 2388357

Acute flaccid myelitis:cause, diagnosis, and management

Acute flaccid myelitis (AFM) is a disabling, polio-like illness mainly affecting children. Outbreaks of MM have occurred across multiple global regions since 2012, and the disease appears to be caused by non-polio enterovirus infection, posing a major public health challenge. The clinical presentation of flaccid and often profound muscle weakness (which can invoke respiratory failure and other critical complications) can mimic several other acute neurological illnesses. There is no single sensitive and specific test for MM, and the diagnosis relies on identification of several important clinical, neuroimaging, and cerebrospinal fluid characteristics. Following the acute phase of AFM, patients typically have substantial residual disability and unique long-term rehabilitation needs. In this Review we describe the epidemiology, clinical features, course, and outcomes of AFM to help to guide diagnosis, management, and rehabilitation. Future research directions include further studies evaluating host and pathogen factors, including investigations into genetic, viral, and immunological features of affected patients, host-virus interactions, and investigations of targeted therapeutic approaches to improve the long-term outcomes in this population