RUSOpt: Robotic UltraSound Probe Normalization with Bayesian Optimization for In-plane and Out-plane Scanning

The one of the significant challenges faced by autonomous robotic ultrasound systems is acquiring high-quality images across different patients. The proper orientation of the robotized probe plays a crucial role in governing the quality of ultrasound images. To address this challenge, we propose a sample-efficient method to automatically adjust the orientation of the ultrasound probe normal to the point of contact on the scanning surface, thereby improving the acoustic coupling of the probe and resulting image quality. Our method utilizes Bayesian Optimization (BO) based search on the scanning surface to efficiently search for the normalized probe orientation. We formulate a novel objective function for BO that leverages the contact force measurements and underlying mechanics to identify the normal. We further incorporate a regularization scheme in BO to handle the noisy objective function. The performance of the proposed strategy has been assessed through experiments on urinary bladder phantoms. These phantoms included planar, tilted, and rough surfaces, and were examined using both linear and convex probes with varying search space limits. Further, simulation-based studies have been carried out using 3D human mesh models. The results demonstrate that the mean ($\pm$SD) absolute angular error averaged over all phantoms and 3D models is $\boldsymbol{2.4\pm0.7^\circ}$ and $\boldsymbol{2.1\pm1.3^\circ}$, respectively.

RoboMal: Malware Detection for Robot Network Systems

Robot systems are increasingly integrating into numerous avenues of modern life. From cleaning houses to providing guidance and emotional support, robots now work directly with humans. Due to their far-reaching applications and progressively complex architecture, they are being targeted by adversarial attacks such as sensor-actuator attacks, data spoofing, malware, and network intrusion. Therefore, security for robotic systems has become crucial. In this paper, we address the underserved area of malware detection in robotic software. Since robots work in close proximity to humans, often with direct interactions, malware could have life-threatening impacts. Hence, we propose the RoboMal framework of static malware detection on binary executables to detect malware before it gets a chance to execute. Additionally, we address the great paucity of data in this space by providing the RoboMal dataset comprising controller executables of a small-scale autonomous car. The performance of the framework is compared against widely used supervised learning models: GRU, CNN, and ANN. Notably, the LSTM-based RoboMal model outperforms the other models with an accuracy of 85% and precision of 87% in 10-fold cross-validation, hence proving the effectiveness of the proposed framework.

Enhancing Safety of Students with Mobile Air Filtration during School Reopening from COVID-19

The paper discusses how robots enable occupant-safe continuous protection for students when schools reopen. Conventionally, fixed air filters are not used as a key pandemic prevention method for public indoor spaces because they are unable to trap the airborne pathogens in time in the entire room. However, by combining the mobility of a robot with air filtration, the efficacy of cleaning up the air around multiple people is largely increased. A disinfection co-robot prototype is thus developed to provide continuous and occupant-friendly protection to people gathering indoors, specifically for students in a classroom scenario. In a static classroom with students sitting in a grid pattern, the mobile robot is able to serve up to 14 students per cycle while reducing the worst-case pathogen dosage by 20%, and with higher robustness compared to a static filter. The extent of robot protection is optimized by tuning the passing distance and speed, such that a robot is able to serve more people given a threshold of worst-case dosage a person can receive.

Learning Multimodal Contact-Rich Skills from Demonstrations Without Reward Engineering

Everyday contact-rich tasks, such as peeling, cleaning, and writing, demand multimodal perception for effective and precise task execution. However, these present a novel challenge to robots as they lack the ability to combine these multimodal stimuli for performing contact-rich tasks. Learning-based methods have attempted to model multi-modal contact-rich tasks, but they often require extensive training examples and task-specific reward functions which limits their practicality and scope. Hence, we propose a generalizable model-free learning-from-demonstration framework for robots to learn contact-rich skills without explicit reward engineering. We present a novel multi-modal sensor data representation which improves the learning performance for contact-rich skills. We performed training and experiments using the real-life Sawyer robot for three everyday contact-rich skills -- cleaning, writing, and peeling. Notably, the framework achieves a success rate of 100% for the peeling and writing skill, and 80% for the cleaning skill. Hence, this skill learning framework can be extended for learning other physical manipulation skills.

Clutter Slices Approach for Identification-on-the-fly of Indoor Spaces

Construction spaces are constantly evolving, dynamic environments in need of continuous surveying, inspection, and assessment. Traditional manual inspection of such spaces proves to be an arduous and time-consuming activity. Automation using robotic agents can be an effective solution. Robots, with perception capabilities can autonomously classify and survey indoor construction spaces. In this paper, we present a novel identification-on-the-fly approach for coarse classification of indoor spaces using the unique signature of clutter. Using the context granted by clutter, we recognize common indoor spaces such as corridors, staircases, shared spaces, and restrooms. The proposed clutter slices pipeline achieves a maximum accuracy of 93.6% on the presented clutter slices dataset. This sensor independent approach can be generalized to various domains to equip intelligent autonomous agents in better perceiving their environment.

Inspection-on-the-fly using Hybrid Physical Interaction Control for Aerial Manipulators

Inspection for structural properties (surface stiffness and coefficient of restitution) is crucial for understanding and performing aerial manipulations in unknown environments, with little to no prior knowledge on their state. Inspection-on-the-fly is the uncanny ability of humans to infer states during manipulation, reducing the necessity to perform inspection and manipulation separately. This paper presents an infrastructure for inspection-on-the-fly method for aerial manipulators using hybrid physical interaction control. With the proposed method, structural properties (surface stiffness and coefficient of restitution) can be estimated during physical interactions. A three-stage hybrid physical interaction control paradigm is presented to robustly approach, acquire and impart a desired force signature onto a surface. This is achieved by combining a hybrid force/motion controller with a model-based feed-forward impact control as intermediate phase. The proposed controller ensures a steady transition from unconstrained motion control to constrained force control, while reducing the lag associated with the force control phase. And an underlying Operational Space dynamic configuration manager permits complex, redundant vehicle/arm combinations. Experiments were carried out in a mock-up of a Dept. of Energy exhaust shaft, to show the effectiveness of the inspection-on-the-fly method to determine the structural properties of the target surface and the performance of the hybrid physical interaction controller in reducing the lag associated with force control phase.

Evaluation of Sampling Methods for Robotic Sediment Sampling Systems

Analysis of sediments from rivers, lakes, reservoirs, wetlands and other constructed surface water impoundments is an important tool to characterize the function and health of these systems, but is generally carried out manually. This is costly and can be hazardous and difficult for humans due to inaccessibility, contamination, or availability of required equipment. Robotic sampling systems can ease these burdens, but little work has examined the efficiency of such sampling means and no prior work has investigated the quality of the resulting samples. This paper presents an experimental study that evaluates and optimizes sediment sampling patterns applied to a robot sediment sampling system that allows collection of minimally-disturbed sediment cores from natural and man-made water bodies for various sediment types. To meet this need, we developed and tested a robotic sampling platform in the laboratory to test functionality under a range of sediment types and operating conditions. Specifically, we focused on three patterns by which a cylindrical coring device was driven into the sediment (linear, helical, and zig-zag) for three sediment types (coarse sand, medium sand, and silt). The results show that the optimal sampling pattern varies depending on the type of sediment and can be optimized based on the sampling objective. We examined two sampling objectives: maximizing the mass of minimally disturbed sediment and minimizing the power per mass of sample. This study provides valuable data to aid in the selection of optimal sediment coring methods for various applications and builds a solid foundation for future field testing under a range of environmental conditions.

Extending Policy from One-Shot Learning through Coaching

Humans generally teach their fellow collaborators to perform tasks through a small number of demonstrations. The learnt task is corrected or extended to meet specific task goals by means of coaching. Adopting a similar framework for teaching robots through demonstrations and coaching makes teaching tasks highly intuitive. Unlike traditional Learning from Demonstration (LfD) approaches which require multiple demonstrations, we present a one-shot learning from demonstration approach to learn tasks. The learnt task is corrected and generalized using two layers of evaluation/modification. First, the robot self-evaluates its performance and corrects the performance to be closer to the demonstrated task. Then, coaching is used as a means to extend the policy learnt to be adaptable to varying task goals. Both the self-evaluation and coaching are implemented using reinforcement learning (RL) methods. Coaching is achieved through human feedback on desired goal and action modification to generalize to specified task goals. The proposed approach is evaluated with a scooping task, by presenting a single demonstration. The self-evaluation framework aims to reduce the resistance to scooping in the media. To reduce the search space for RL, we bootstrap the search using least resistance path obtained using resistive force theory. Coaching is used to generalize the learnt task policy to transfer the desired quantity of material. Thus, the proposed method provides a framework for learning tasks from one demonstration and generalizing it using human feedback through coaching.

Self-Evaluation in One-Shot Learning from Demonstration of Contact-Intensive Tasks

Humans naturally "program" a fellow collaborator to perform a task by demonstrating the task few times. It is intuitive, therefore, for a human to program a collaborative robot by demonstration and many paradigms use a single demonstration of the task. This is a form of one-shot learning in which a single training example, plus some context of the task, is used to infer a model of the task for subsequent execution and later refinement. This paper presents a one-shot learning from demonstration framework to learn contact-intensive tasks using only visual perception of the demonstrated task. The robot learns a policy for performing the tasks in terms of a priori skills and further uses self-evaluation based on visual and tactile perception of the skill performance to learn the force correspondences for the skills. The self-evaluation is performed based on goal states detected in the demonstration with the help of task context and the skill parameters are tuned using reinforcement learning. This approach enables the robot to learn force correspondences which cannot be inferred from a visual demonstration of the task. The effectiveness of this approach is evaluated using a vegetable peeling task.

ExpressionBot: An Emotive Lifelike Robotic Face for Face-to-Face Communication

This article proposes an emotive lifelike robotic face, called ExpressionBot, that is designed to support verbal and non-verbal communication between the robot and humans, with the goal of closely modeling the dynamics of natural face-to-face communication. The proposed robotic head consists of two major components: 1) a hardware component that contains a small projector, a fish-eye lens, a custom-designed mask and a neck system with 3 degrees of freedom; 2) a facial animation system, projected onto the robotic mask, that is capable of presenting facial expressions, realistic eye movement, and accurate visual speech. We present three studies that compare Human-Robot Interaction with Human-Computer Interaction with a screen-based model of the avatar. The studies indicate that the robotic face is well accepted by users, with some advantages in recognition of facial expression and mutual eye gaze contact.