Ultrasound scanning is an efficient imaging modality preferred for quick medical procedures. However, due to the lack of skilled sonographers, researchers have developed many Robotic Ultrasound System (RUS) prototypes for various procedures. Most of these systems have a human-in-the-loop and require an expert to point the robot to the region of the subject to be scanned. Only a few systems try to incorporate some knowledge from the exterior shape of the subject for ultrasound scanning. Accurate 3D surface reconstruction of a patient's exterior can enable an RUS to perceive subjects more like a clinician would. It can help localize the subject for the robot while eliminating input from an expert. Ultrasound scanning trajectories can be better planned if the RUS first detects critical regions on the surface of the subject and corresponding curvatures. We use an RGB-D sensor to acquire point clouds representing the 3D surface of the subject, which in the present work is for a lower-torso leg phantom. A consolidated pipeline for creating an optimized 3D surface reconstruction of a subject is presented and is used to autonomously identify a region of interest for scanning femoral vessels with an ultrasound probe. To make our system more robust to inter-subject variations in shape and size, we incorporate a trajectory optimization module of the RUS-mounted RGB-D sensor. To this end, we introduce a comprehensive evaluation score to quantify the quality of point cloud reconstructions. The resulting improvements in 3D surface scanning and reconstruction enable near-automation in generating ultrasound scanning trajectories for femoral vessels. Our pipeline produces ultrasound images with an average ZNCC score of 0.86 and our 3D point cloud reconstructions are accurate up to le-5 m from a ground-truth high-resolution CT scan.

This paper presents a deep-learning model for deformable registration of ultrasound images at online rates, which we call U-RAFT. As its name suggests, U-RAFT is based on RAFT, a convolutional neural network for estimating optical flow. U-RAFT, however, can be trained in an unsupervised manner and can generate synthetic images for training vessel segmentation models. We propose and compare the registration quality of different loss functions for training U-RAFT. We also show how our approach, together with a robot performing force-controlled scans, can be used to generate synthetic deformed images to significantly expand the size of a femoral vessel segmentation training dataset without the need for additional manual labeling. We validate our approach on both a silicone human tissue phantom as well as on in-vivo porcine images. We show that U-RAFT generates synthetic ultrasound images with 98% and 81% structural similarity index measure (SSIM) to the real ultrasound images for the phantom and porcine datasets, respectively. We also demonstrate that synthetic deformed images from U-RAFT can be used as a data augmentation technique for vessel segmentation models to improve intersection-over-union (IoU) segmentation performance.

Ultrasound scanning is an imaging technique that aids medical professionals in diagnostics and interventional procedures. However, a trained human-in-the-loop (HITL) with a radiologist is required to perform the scanning procedure. We seek to create a novel ultrasound system that can provide imaging in the absence of a trained radiologist, say for patients in the field who suffered injuries after a natural disaster. One challenge of automating ultrasound scanning involves finding the optimal area to scan and then performing the actual scan. This task requires simultaneously maintaining contact with the surface while moving along it to capture high quality images. In this work, we present an automated Robotic Ultrasound System (RUS) to tackle these challenges. Our approach introduces a Bayesian Optimization framework to guide the probe to multiple points on the unknown surface. Our proposed framework collects the ultrasound images as well as the pose information at every probed point to estimate regions with high vessel density (information map) and the surface contour. Based on the information map and the surface contour, an area of interest is selected for scanning. Furthermore, to scan the proposed region, a novel 6-axis hybrid force-position controller is presented to ensure acoustic coupling. Lastly, we provide experimental results on two different phantom models to corroborate our approach.

Effectively disassembling and recovering materials from waste electrical and electronic equipment (WEEE) is a critical step in moving global supply chains from carbon-intensive, mined materials to recycled and renewable ones. Conventional recycling processes rely on shredding and sorting waste streams, but for WEEE, which is comprised of numerous dissimilar materials, we explore targeted disassembly of numerous objects for improved material recovery. Many WEEE objects share many key features and therefore can look quite similar, but their material composition and internal component layout can vary, and thus it is critical to have an accurate classifier for subsequent disassembly steps for accurate material separation and recovery. This work introduces RGB-X, a multi-modal image classification approach, that utilizes key features from external RGB images with those generated from X-ray images to accurately classify electronic objects. More specifically, this work develops Iterative Class Activation Mapping (iCAM), a novel network architecture that explicitly focuses on the finer-details in the multi-modal feature maps that are needed for accurate electronic object classification. In order to train a classifier, electronic objects lack large and well annotated X-ray datasets due to expense and need of expert guidance. To overcome this issue, we present a novel way of creating a synthetic dataset using domain randomization applied to the X-ray domain. The combined RGB-X approach gives us an accuracy of 98.6% on 10 generations of modern smartphones, which is greater than their individual accuracies of 89.1% (RGB) and 97.9% (X-ray) independently. We provide experimental results133Experimental work done at Biorobotics Lab, Robotics Institute, Carnegie Mellon University to corroborate our results.

In this work, we provide a simulation framework to perform systematic studies on the effects of spinal joint compliance and actuation on bounding performance of a 16-DOF quadruped spined robot Stoch 2. Fast quadrupedal locomotion with active spine is an extremely hard problem, and involves a complex coordination between the various degrees of freedom. Therefore, past attempts at addressing this problem have not seen much success. Deep-Reinforcement Learning seems to be a promising approach, after its recent success in a variety of robot platforms, and the goal of this paper is to use this approach to realize the aforementioned behaviors. With this learning framework, the robot reached a bounding speed of 2.1m /s with a maximum Froude number of 2. Simulation results also show that use of active spine, indeed, increased the stride length, improved the cost of transport, and also reduced the natural frequency to more realistic values.

In this paper, we explore a specific form of deep reinforcement learning (D-RL) technique for quadrupedal walking—trajectory based policy search via deep policy networks. Existing approaches determine optimal policies for each time step, whereas we propose to determine an optimal policy for each walking step. We justify our approach based on the fact that animals including humans use “low” dimensional trajectories at the joint level to realize walking. We will construct these trajectories by using Bézier polynomials, with the coefficients being determined by a parameterized policy. In order to maintain smoothness of the trajectories during step transitions, hybrid invariance conditions are also applied. The action is computed at the beginning of every step, and a linear PD control law is applied to track at the individual joints. After each step, reward is computed, which is then used to update the new policy parameters for the next step. After learning an optimal policy, i.e., an optimal walking gait for each step, we then successfully play them in a custom built quadruped robot, Stoch 2, thereby validating our approach.