Cardiac disease, including atrial fibrillation (AF), is one of the biggest causes of morbidity and mortality in the world, accounting for one third of all deaths. Cardiac modelling is now a well-established field.

Cassie the robot, invented at Oregon State University and produced by OSU spinout company Agility Robotics, has made history by traversing 5 kilometres outdoors in just over 53 minutes. The robot was developed under the direction of robotics professor Jonathan Hurst with a 16-month, $1 million grant from the Advanced Research Projects Agency of the U.S. Department of Defense. Since Cassie's introduction in 2017, OSU students funded by the National Science Foundation have been exploring machine learning options for the robot. "The Dynamic Robotics Laboratory students in the OSU College of Engineering combined expertise from biomechanics and existing robot control approaches with new machine learning tools," said Hurst, who co-founded Agility in 2017. "This type of holistic approach will enable animal-like levels of performance.

CORVALLIS, Ore. – Cassie the robot, invented at Oregon State University and produced by OSU spinout company Agility Robotics, has made history by traversing 5 kilometers, completing the route in just over 53 minutes. Cassie was developed under the direction of robotics professor Jonathan Hurst with a 16-month, $1 million grant from the Defense Advanced Research Projects Agency, or DARPA. Since Cassie's introduction in 2017, in collaboration with artificial intelligence professor Alan Fern OSU students funded by the National Science Foundation and the DARPA Machine Common Sense program have been exploring machine learning options for the robot. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State's campus untethered and on a single battery charge. "The Dynamic Robotics Laboratory students in the OSU College of Engineering combined expertise from biomechanics and existing robot control approaches with new machine learning tools," said Hurst, who co-founded Agility in 2017.

CORVALLIS, Ore. (KTVZ) – Cassie the robot, invented at Oregon State University and produced by OSU spinout company Agility Robotics, has made history by traversing 5 kilometers, completing the route in just over 53 minutes. Cassie was developed under the direction of robotics professor Jonathan Hurst with a 16-month, $1 million grant from the Advanced Research Projects Agency of the U.S. Department of Defense. Since Cassie's introduction in 2017, OSU students funded by the National Science Foundation have been exploring machine learning options for the robot. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State's campus untethered and on a single battery charge. "The Dynamic Robotics Laboratory students in the OSU College of Engineering combined expertise from biomechanics and existing robot control approaches with new machine learning tools," said Hurst, who co-founded Agility in 2017.

With the rapidly growing use of Convolutional Neural Networks (CNNs) in real-world applications related to machine learning and Artificial Intelligence (AI), several hardware accelerator designs for CNN inference and training have been proposed recently. In this paper, we present ATRIA, a novel bit-pArallel sTochastic aRithmetic based In-DRAM Accelerator for energy-efficient and high-speed inference of CNNs. ATRIA employs light-weight modifications in DRAM cell arrays to implement bit-parallel stochastic arithmetic based acceleration of multiply-accumulate (MAC) operations inside DRAM. ATRIA significantly improves the latency, throughput, and efficiency of processing CNN inferences by performing 16 MAC operations in only five consecutive memory operation cycles. We mapped the inference tasks of four benchmark CNNs on ATRIA to compare its performance with five state-of-the-art in-DRAM CNN accelerators from prior work. The results of our analysis show that ATRIA exhibits only 3.5% drop in CNN inference accuracy and still achieves improvements of up to 3.2x in frames-per-second (FPS) and up to 10x in efficiency (FPS/W/mm2), compared to the best-performing in-DRAM accelerator from prior work.

Robots have walked on legs for decades. Today's most advanced humanoid robots can tramp along flat and inclined surfaces, climb up and down stairs, and slog through rough terrain. But despite the progress, legged robots still can't begin to match the agility, efficiency, and robustness of humans and animals. Existing walking robots hog power and spend too much time in the shop. All too often, they fail, they fall, and they break. For the robotic helpers we've long dreamed of to become a reality, these machines will have to learn to walk as we do. We must build robots with legs because our world is designed for legs.

Atrial Fibrillation (AF) is a common electro-physiological cardiac disorder that causes changes in the anatomy of the atria. A better characterization of these changes is desirable for the definition of clinical biomarkers, and thus there is a need of its fully automatic segmentation from clinical images. In this work we present an architecture based in 3D-convolution kernels, a Volumetric Fully Convolution Neural Network (V-FCNN), able to segment the entire volume in one-shot, and consequently integrate the implicit spatial redundancy present in high resolution images. A loss function based on the mixture of both Mean Square Error (MSE) and Dice Loss (DL) is used, in an attempt to combine the ability to capture the bulk shape and the reduction of local errors products by over segmentation. Results demonstrate a reasonable performance in the middle region of the atria, and the impact of the challenges of capturing the variability of the pulmonary veins or the identification of the valve plane that separates the atria to the ventricle.

From Robot Dogs and Bees yo cyborg sea creatures, these 10 robotic animals are like something out of Black mirror! Mimicking a real ant colony, the BionicAnts work together to achieve common tasks and goals.The Ants communicate with each other, like actual ants, and work to complete complex tasks and trials, demonstrating how a single autonomous robot can come to work with a group when need be. This cooperative technology could be used to one day automatize dangerous jobs, potentially saving human lives without sacrificing the quality of work that is expected. The ATRIAS is a two legged robot with attributes similar to Ostriches or other grounded birds. It is incredibly fast and agile yet also stable.

Editor's note: This is part of our series HardWIRED: Welcome to the Robotic Future, in which we explore the fascinating machines transforming society. A central tension in robotics is that while humanity created the robot in its image--the classic humanoid biped of sci-fi--those two-legged machines are particularly hard to build. Getting a bipedal robot to not fall on its face, much less walk, is a feat that no one has mastered. Roboticists are getting there, though. Take, for instance, a robot called Cassie from Agility Robotics.

Today, Agility Robotics, a spin-off of Oregon State University, is officially announcing a shiny new bipedal robot named Cassie. Cassie is a dynamic walker, meaning that it walks much more like humans do than most of the carefully plodding bipedal robots we're used to seeing. This makes it better at handling the kind of diverse and complex terrain that we walk over all the time without even thinking, a talent that's going to be mandatory for robots that want to tackle the different environments and situations that they'll need to master to be actually useful around people. In addition to search-and-rescue and disaster relief, Agility Robotics has one particular environment and situation in mind: They want Cassie to be scampering up your steps to deliver packages to your front door. Cassie is just three months old in this video, which, if you consider the typical pace for teaching a bipedal robot that you designed from the ground up from scratch to walk without constantly falling over, is quite frankly astonishing.