Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
The death of a pedestrian who was struck by an autonomous vehicle in Tempe, Arizona, has brought fresh scrutiny to the accelerating development of self-driving cars. The accident on March 18 is bound to be studied exhaustively, both to determine fault and to assess and refine the overall safety of autonomous systems. According to accounts of the accident, the vehicle, outfitted to test Uber's autonomous driving system, struck a woman at night as she pushed her bicycle across a road outside of a designated crosswalk. Video of the crash, released by Tempe police, shows a woman emerging from a darkened area seconds before she was struck; in the same span of time, the safety driver looks down multiple times for reasons that aren't clear. Uber pledged its full cooperation in the unfolding investigation but has already reached a settlement with some of the victim's family members, while others have come forward, according to multiple news reports.
Gaps in maps are a problem, particularly for systems being developed for self-driving cars. To address the issue, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created RoadTracer, an automated method to build road maps that's 45 percent more accurate than existing approaches. Using data from aerial images, the team says that RoadTracer is not just more accurate, but more cost-effective than current approaches. MIT professor Mohammad Alizadeh says that this work will be useful both for tech giants like Google and for smaller organizations without the resources to curate and correct large amounts of errors in maps. "RoadTracer is well-suited to map areas of the world where maps are frequently out of date, which includes both places with lower population and areas where there's frequent construction," says Alizadeh, one of the co-authors of a new paper about the system.
Map apps may have changed our world, but they still haven't mapped all of it yet. Specifically, mapping roads can be difficult and tedious: Even after taking aerial images, companies still have to spend many hours manually tracing out roads. As a result, even companies like Google haven't yet gotten around to mapping the vast majority of the more than 20 million miles of roads across the globe. Gaps in maps are a problem, particularly for systems being developed for self-driving cars. To address the issue, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created RoadTracer, an automated method to build road maps that's 45 percent more accurate than existing approaches.
Traditionally, some manufacturers have avoided robots because of the cost. However, a new robots-for-hire business model is changing that misperception and enabling many types of companies to benefit from automation. Large and small manufacturers in a wide variety of industries are now beginning to treat robots as "temp workers." It's part of a new trend called Robotics as a Service (RaaS).
Pony.ai self-driving cars during a trial run in February in Guangzhou, China. Driverless cars could become a regular feature of the roads as early as April – at least in California, which has decided to allow fully autonomous vehicles to be tested on the roads (none of those pesky humans who have been present in test drives so far). Arizona has already become a fair-weather center for testing driverless vehicles, thanks in large part to the governor's support, and Uber announced last week that it has finished testing its self-driving trucks in Arizona and is now beginning to use them to move goods across the state. The British government launched a review last week of laws governing self-driving vehicles, with the aim of getting autonomous cars on the road by 2021, and other countries around the world are also experimenting with autonomous vehicles. Clearly a big step for the technology and automotive industries – no surprise that companies working on driverless vehicles include Google and Uber, as well as traditional automakers like Audi, BMW, Ford, GM, Volkswagen and Volvo – the advent of human-less driving could also redirect the traffic of our days, how we live our lives and get around.
Already, the electronic brains of the most advanced robotic models surpass human intelligence and are able to do things that will make some of us shudder uncomfortably. But what is your reaction going to be after learning about recent advances in robotics and artificial intelligence? Scientists at the University of Texas (Austin) have simulated mental illness for a computer, testing schizophrenia on artificial intelligence units. The test subject is DISCERN – a supercomputer that functions as a biological neural network and operates using the principles of how human brain functions. In their attempt to recreate the mechanism behind schizophrenia, the scientists have applied the concepts described in the theory of hyper-learning, which states that schizophrenic brain processes and stores too much information too thoroughly by memorizing everything, even the unnecessary details.
Amiri, Saeid (Cleveland State University) | Wei, Suhua (Cleveland State University) | Zhang, Shiqi (Cleveland State University) | Sinapov, Jivko (Tufts University) | Thomason, Jesse (The University of Texas at Austin) | Stone, Peter (The University of Texas at Austin)
Intelligent robots frequently need to explore the objects in their working environments. Modern sensors have enabled robots to learn object properties via perception of multiple modalities. However, object exploration in the real world poses a challenging trade-off between information gains and exploration action costs. Mixed observability Markov decision process (MOMDP) is a framework for planning under uncertainty, while accounting for both fully and partially observable components of the state. Robot perception frequently has to face such mixed observability. This work enables a robot equipped with an arm to dynamically construct query-oriented MOMDPs for object exploration. The robot’s behavioral policy is learned from two datasets collected using real robots. Our approach enables a robot to explore object properties in a way that is significantly faster while improving accuracies in comparison to existing methods that rely on hand-coded exploration strategies.
Industrial robots are primarily known from the automotive industry's production lines. The goal of this class is to present robots instead as multifunctional and flexible interfaces between the digital and the physical world that can be used for anything from innovative, large-scale fabrication to immersive virtual reality (VR) simulators. This extension beyond the robots' initial scope is enabled by new software developments that facilitate a seamless workflow from design to machine through Dynamo software and KUKA prc. Utilizing parametric design tools lets us use robots for mass customization and small lot sizes, rather than mass fabrication. The class will provide an overview on how to utilize industrial robots through Dynamo and Fusion 360 software, and present realized projects by both small to medium-size enterprises as well as international corporations.
It's okay for me, since I did not put that much effort in it.The I feel bad for the other students though. So there are two electives, semantic segmentation, and functional safety. Functional safety is interesting but I chose semantic segmentation, because it is a coding project, the functional safety project is to write a document. I learned about the concept of functional safety, and functional safety frameworks to ensure that vehicles is safe, both at the system and component levels.
Using digitization to improve how things get done has boosted revenue by reducing downtime in connected factories. The ability to immediately analyze and respond to process upsets, and the possibility to more accurately conduct scheduled maintenance help manufacturers to save money and to increase productivity. As more plants become smarter, manufacturers need a solution "to watch the robots." In today's interview, Norman Fast, CEO of Industrial Video & Control Co. will explain how IIoT is changing industrial network video solutions and what are the current demands. Lucian Fogoros: How did IVCCO come to produce video solutions for industrial applications?