Trajectory prediction forecasts nearby agents' moves based on their historical trajectories. Accurate trajectory prediction is crucial for autonomous vehicles. Existing attacks compromise the prediction model of a victim AV by directly manipulating the historical trajectory of an attacker AV, which has limited real-world applicability. This paper, for the first time, explores an indirect attack approach that induces prediction errors via attacks against the perception module of a victim AV. Although it has been shown that physically realizable attacks against LiDAR-based perception are possible by placing a few objects at strategic locations, it is still an open challenge to find an object location from the vast search space in order to launch effective attacks against prediction under varying victim AV velocities. Through analysis, we observe that a prediction model is prone to an attack focusing on a single point in the scene. Consequently, we propose a novel two-stage attack framework to realize the single-point attack. The first stage of prediction-side attack efficiently identifies, guided by the distribution of detection results under object-based attacks against perception, the state perturbations for the prediction model that are effective and velocity-insensitive. In the second stage of location matching, we match the feasible object locations with the found state perturbations. Our evaluation using a public autonomous driving dataset shows that our attack causes a collision rate of up to 63% and various hazardous responses of the victim AV. The effectiveness of our attack is also demonstrated on a real testbed car. To the best of our knowledge, this study is the first security analysis spanning from LiDAR-based perception to prediction in autonomous driving, leading to a realistic attack on prediction. To counteract the proposed attack, potential defenses are discussed.

Wiping behavior is a task of tracing the surface of an object while feeling the force with the palm of the hand. It is necessary to adjust the force and posture appropriately considering the various contact conditions felt by the hand. Several studies have been conducted on the wiping motion, however, these studies have only dealt with a single surface material, and have only considered the application of the amount of appropriate force, lacking intelligent movements to ensure that the force is applied either evenly to the entire surface or to a certain area. Depending on the surface material, the hand posture and pressing force should be varied appropriately, and this is highly dependent on the definition of the task. Also, most of the movements are executed by high-rigidity robots that are easy to model, and few movements are executed by robots that are low-rigidity but therefore have a small risk of damage due to excessive contact. So, in this study, we develop a method of motion generation based on the learned prediction of contact force during the wiping motion of a low-rigidity robot. We show that MyCobot, which is made of low-rigidity resin, can appropriately perform wiping behaviors on a plane with multiple surface materials based on various task definitions.

Soft robots have the ability to adapt to their environment, which makes them suitable for use in disaster areas and agricultural fields, where their mobility is constrained by complex terrain. One of the main challenges in developing soft terrestrial robots is that the robot must be soft enough to adapt to its environment, but also rigid enough to exert the required force on the ground to locomote. In this paper, we report a pneumatically driven, soft modular appendage made of silicone for a terrestrial robot capable of generating specific mechanical movement to locomote and transport loads in the desired direction. This two-segmented soft appendage uses actuation in between the joint and the lower segment of the appendage to ensure adequate rigidity to exert the required force to locomote. A prototype of a soft-rigid-bodied tethered physical robot was developed and two sets of experiments were carried out in both air and underwater environments to assess its performance. The experimental results address the effectiveness of the soft appendage to generate adequate force to navigate through various environments and our design method offers a simple, low-cost, and efficient way to develop terradynamically capable soft appendages that can be used in a variety of locomotion applications.

Andrew Fischer was facing a conundrum. Gamers sink literally hundreds of hours into Bethesda's Fallout games. The twisted steel and charred homesteads sprawl out in every direction, and it's hard to feel fully satiated by your save file until you've explored every square inch of the atlas. To truly appreciate Fallout, one must commune with the ghouls, and ride with the raiders, and spelunk through blown-out cafeterias and coffee shops long before we see the credits roll. Hell, sometimes we even set the side quests aside in order to bask peacefully under the wide open night sky during the brief breaks between super mutant assaults.

For decades researchers have envisioned a world where digital user interfaces are seamlessly integrated with the physical environment, until the two are virtually indistinguishable from one another. This vision, though, is held up by a few boundaries. First, it's difficult to integrate sensors and display elements into our tangible world due to various design constraints. Second, most methods to do so are limited to smaller scales, bound by the size of the fabricating device. Recently, a group of researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with SprayableTech, a system that lets users create room-sized interactive surfaces with sensors and displays.

Today, the recycling market is changing rapidly due to global changes where the quality requirements of the incoming and outgoing material are increased. As a result, systems that are separating waste material from the target material need to be improved continuously to cope with this change. Stora Enso's Langerbrugge Mill in northwest Belgium, which is one of the largest paper mills in Europe, required a more effective paper-cardboard sorting solution and technology that can easily be retrained for anomaly detection. This technology was developed by Robovision, a company specializing in deep learning-based machine vision and robot programming, and Imec, which is the world-leading R&D and innovation hub in nanoelectronics and digital technologies within the framework of the AUTOWARE project. A big challenge in paper recycling is the separation of cardboard and waste materials from paper.

It's a modern day ritual practiced by some of the most passionate fans on the planet: gathering to observe the reveal of new video games. In June, some of the devoted assembled to pay tribute at Nintendo's Rockefeller Center store. Many wore Nintendo t-shirts, hats and other swag. The most hardcore dressed as their favorite characters, including one devotee in full-blown Luigi garb. They were there to watch a livestream of the company's latest "Nintendo Direct," a slickly-produced video announcing upcoming games and more, and get hands-on time with just-announced titles like Link's Awakening, a remake of a 1993 classic.

Cities around the world have an increasing number of inhabitants, and when the number of people in an area increases also the production of garbage is increased. This created the dilemma of collecting this garbage. By making this process more efficient the garbage will be on city streets for a shorter duration and thus have less negative effects on the environment. When garbage can be detected in a timely manner this will allow for a more efficient reaction from the local government, which in turn can deploy the correct resources to solve the problem. For example a truck that can pick up bulky waste or enforcers that can enforce when local regulations are broken.

Ford is recycling over one billion plastic bottles every year to develop elements of he car's interior, reducing the amount of plastic ending up in a landfill. The American car maker has revealed that their Romanian-built EcoSport SUVs' carpets are made using 470 single-use bottles from recycled plastic bottles. The combined weight is said to weigh an estimated 8,262 metric tons and, if they were laid end to end, would stretch more than twice around the world, they said. Plastic fantastic: Ford has revealed that its EcoSport SUV features carpets that are made from recycled plastic bottles. According to the United Nations Environmental Agency, the world produces around 300 million tons of plastic each year, half of which is single-use items.

Zume, the robotic pizza maker, is now valued at more than US $2 billion, thanks to its latest round of investment. According to The Wall Street Journal, this latest infusion of funds--$375 million--came entirely from SoftBank; and the Japanese conglomerate apparently has another $375 million at the ready should Zume need it. The valuation, in Silicon Valley terms, makes the new company a unicorn, one of the rare breed of startups thought to be worth over $1 billion. Zume, based in Mountain View, Calif., launched three years ago. The company set out to revolutionize pizza delivery by turning pizza-making over to robots, and then cooking the pizza in the back of delivery vans in ovens controlled through cloud-based software.