Optical coherence tomography angiography (OCTA) is an imaging modality that can be used for analyzing retinal vasculature. Quantitative assessment of en face OCTA images requires accurate segmentation of the capillaries. Using deep learning approaches for this task faces two major challenges. First, acquiring sufficient manual delineations for training can take hundreds of hours. Second, OCTA images suffer from numerous contrast-related artifacts that are currently inherent to the modality and vary dramatically across scanners. We propose to solve both problems by learning a disentanglement of an anatomy component and a local contrast component from paired OCTA scans. With the contrast removed from the anatomy component, a deep learning model that takes the anatomy component as input can learn to segment vessels with a limited portion of the training images being manually labeled. Our method demonstrates state-of-the-art performance for OCTA vessel segmentation.

Precomputed roadmaps can enable effective multi-query motion planning: a roadmap can be built for a robot as if no obstacles were present, and then after edges invalidated by obstacles observed at query time are deleted, path search through the remaining roadmap returns a collision-free plan. However, large roadmaps are memory intensive to store, and can be too slow for practical use. We present an algorithm for compressing a large roadmap so that the collision detection phase fits into a computational budget, while retaining a high probability of finding high-quality paths. Our algorithm adapts work from graph theory and data mining by treating roadmaps as unreliable networks, where the probability of edge failure models the probability of a query-time obstacle causing a collision. We experimentally evaluate the quality of the resulting roadmaps in a suite of four motion planning benchmarks.

We have designed a programmable architecture to accelerate collision detection and graph search, two of the principal components of robotic motion planning. The programmability enables the architecture to be applied to a wide range of different robots and motion planning applications. We present the architecture of our accelerator and describe and evaluate its microarchitecture implementation.

We have developed a hardware accelerator for motion planning, a critical operation in robotics. In this paper, we present the microarchitecture of our accelerator and describe a prototype implementation on an FPGA. We experimentally show that the accelerator improves performance by three orders of magnitude and improves power consumption by more than one order of magnitude. These gains are achieved through careful hardware/software co-design. We modify conventional motion planning algorithms to aggressively precompute collision data, as well as implement a microarchitecture that leverages the parallelism present in the problem.