Dawn M. Tilbury’s research while affiliated with Concordia University Ann Arbor and other places

After-action reviews (AARs) are professional discussions that help operators and teams enhance their task performance by analyzing completed missions with peers and professionals. Previous studies comparing different formats of AARs have focused mainly on human teams. However, the inclusion of robotic teammates brings along new challenges in understanding teammate intent and communication. Traditional AAR between human teammates may not be satisfactory for human-robot teams. To address this limitation, we propose a new training review (TR) tool, called the Virtual Spectator Interface (VSI), to enhance human-robot team performance and situational awareness (SA) in a simulated search mission. The proposed VSI primarily utilizes visual feedback to review subjects' behavior. To examine the effectiveness of VSI, we took elements from AAR to conduct our own TR, and designed a 1 × 3 between-subjects experiment with experimental conditions: TR with (1) VSI, (2) screen recording, and (3) non-technology (only verbal descriptions). The results of our experiments demonstrated that the VSI did not result in significantly better team performance than other conditions. However, the TR with VSI led to more improvement in the subjects' SA over the other conditions. Training review, After-action review, human-robot team-ing, team performance, and situation awareness.

The capability of robots to rapidly adapt to new tasks without extensive reprogramming offers significant flexibility in reconfiguration of manufacturing processes to cope with unforeseen events. In modern manufacturing environments where numerous hardware and software systems exchange data with each other to perform a myriad of tasks, modularizing sub-systems and reusing commonly available information like product CAD models can increase robustness and efficiency of the reconfiguration. Yet, current approaches for robotic grasping tend to focus on standalone vision-based learning that often require either retraining to adapt to new object categories or massive dataset not available in manufacturing environments, making generalization challenging. This paper addresses the problem of exploiting available information, like CAD models, in manufacturing settings to efficiently generate a tractable set of grasps for known rigid objects, which can be directly applied to a wide class of robotic manipulations. In order to quickly produce diverse grasp configurations for arbitrary geometric models, we present GraspMixer, a combination of (1) an efficient offline sampler that utilizes specifications of a parallel-jaw gripper, and (2) a mapping function that fuses multiple features of a grasp to output a binary quality metric. During evaluation using physics-based simulations, a robotic gripper successfully executes 92.9% of all grasp configurations for 12 novel objects selected by GraspMixer. Among five different grasp sampling methods, GraspMixer also achieves the highest grasp success rate when performing table-top single object grasping under object pose uncertainty. The computation of this offline pipeline takes less than 1.0 minutes for each object without GPU hardware acceleration, which is comparable to or outperforms most of the benchmarks in the evaluation. Importantly, our framework exhibits impressive simulation-to-reality adaptation, achieving over 95% grasp success rate on previously unseen novel objects. All of these results are achieved with fewer than 10% of the samples typically used by other learning-based grasping techniques.

title>ABSTRACT Although autonomy has the potential to help military drivers travel safely while performing other tasks, many drivers refuse to rely on the technology. Military drivers sometimes fail to leverage a vehicle’s autonomy because of a lack of trust. To address this issue, the current study examines whether augmenting the driver’s situational awareness will promote their trust in the autonomy. Results of this study are expected to provide new insights into promoting trust and acceptance of autonomy in military settings.</p

title>ABSTRACT Autonomous driving systems (ADS) in autonomous and semi-autonomous vehicles have the potential to improve driving safety and enable drivers to perform non-driving tasks concurrently. Drivers sometimes fail to fully leverage a vehicle’s autonomy because of a lack of trust. To address this issue, the present study examined the influence of risk on drivers’ trust. Subject tests were conducted to evaluate the effects of combined internal and external risk, where participants drove a simulated semi-autonomous vehicle and completed a secondary task at the same time. Results of this study are expected to provide new insights into promoting trust and acceptance of autonomy in both military and civilian settings.</p

This study investigates the impact of a supportive system on takeover transitions in conditionally automated driving (SAE Level 3). The supportive system is designed to direct drivers’ attention toward potential hazards in the environment when a takeover request occurs. The study comprises two components: (a) identifying various types of potential hazards using naturalistic driving data, and (b) conducting a driving simulator study to develop and assess a gaze guidance system based on the N-SEEV model of visual attention. Results indicate that drivers using a highly salient attention guidance system were less likely to collide with a secondary hazard during takeover transitions. This suggests that gaze guidance support is an effective approach for assisting drivers during takeover transitions.

This study introduces and validates a new metric for assessing takeover performance in conditionally automated driving, using Fréchet Distance. Fréchet Distance is a measurement that measures the similarity between two separate curves. Thirty-two participants took part in a simulated driving experiment. Employing a 2 × 2 within-subjects design, the study compared traditional takeover performance metrics, including takeover time, time to collision, and resulting acceleration, with Fréchet Distance. Analysis results revealed similar trends between traditional metrics and Fréchet Distance. These findings suggest that Fréchet Distance can effectively measure takeover performance by integrating spatial and temporal aspects.

This paper explores the problem of automated robot program generation from limited historical data when neither accurate geometric environmental models nor online vision feedback are available. The Program Reuse Framework (PRF) is developed, which uses expert-defined motion classes, a novel data structure introduced in this work, to learn affordances, workspaces, and skills from historical data. Historical data comprise raw robot joint trajectories and descriptions of the robot task being completed. Given new tasks, motion classes are then used again to formulate an optimization problem capable of generating new open-loop, skill-based programs to complete the tasks. To cope with a lack of geometric models, a technique to learn safe workspaces from demonstrations is developed, allowing the risk of new programs to be estimated before execution. A new learnable motion primitive for redundant manipulators is introduced, called a redundancy dynamical movement primitive, which enables new end-effector goals to be reached while mimicking the whole-arm behavior of a demonstration. A mobile manipulator part transportation task is used throughout to illustrate each step of the framework.