A bill that would speed up development of self-driving cars and establish a federal framework for their regulation, the Highly Automated Vehicle Testing and Deployment Act of 2017, is now working its way through Congress. But they're also willing to expose vehicles via online software updates because the logistical challenges posed by physical downloads (car drives to shop, shop downloads new software) would make the frequent improvements required to millions and millions of lines of code virtually impossible to effect. Geater explained that some of the measures being taken to improve security include separating functions – the sound system can communicate with the vehicle speed system (to modulate sound volume according to vehicle speed), but neither can communicate with the transmission, for example. "People prove time and time again to be absolutely terrible, dangerous drivers," Geater said, adding that the risks posed by an actual human behind the wheel of a car far outweigh those posed by a potential hacker.
To explore those questions, Masuda and his colleagues at Osaka University built a three-legged robot named Martian. A decade ago, Dennis Hong and his team at RoMeLa developed a tripedal robot called STriDER, the Self-excited Tripedal Dynamic Experimental Robot, which they used to test a variety of tripedal gaits. It's no different with the tripod robot: Depending on what oscillation frequency is selected, the robot can be made to either rotate or move in a single direction, without having that specific gait programmed into it. While this is the third generation of Martian robot the Osaka University researchers have built, it was intentionally designed with very simple dynamics to make it easier to approach the gait generation problem.
To solve this problem, Amazon is making it someone else's problem, by hosting a yearly robotics "picking" challenge. What Amazon was looking for was a robot that could identify items, remove target items from storage and place them into boxes (picking), take target items from totes and place them into storage (stowing), and then do both at once in a grand fantastic explosion all-or-nothing final competition. Here's an overview of how things went: Team ACRV (from the Australian Centre for Robotic Vision at Queensland University of Technology in Australia), which didn't place in the top three on either the individual pick task or stow task, managed to knock it out of the park on the combined final task, taking first place and going home with US $80,000 (which is way more in Australia). While QUT's press release suggests that "the team has solved a key robotics problem for Amazon-- picking items and stowing them in boxes in an unstructured environment," that strikes us as awfully optimistic.
After reports earlier this week that iRobot would sell data gathered about a user's home from Roomba vacuums, company CEO Colin Angle insists that iRobot will "never" sell customer data, ZDNet reported. The promise is in stark contrast to what Angle appeared to suggest earlier in the week in an interview with Reuters during which the executive said the company could use mapping data gathered by its robotic vacuums and sell them to companies looking to gain insight about how people use smart home devices. The company also took the opportunity to clarify how its current vacuuming robots store data gathered from a person's home. According to the company, its Roomba 900 series vacuums capture mapping and navigation information and store in on the robot.
It's why the company has invited a motley crew of mechanical arms, grippers, suction cups--and their human handlers--to Nagoya, Japan, this week to show off their manipulation skills. The Amazon Robotics Challenge starts Thursday and tasks teams with picking up objects ranging from towels to toilet brushes and moving them between storage bins and boxes. Although Amazon might like to offer gainful employment to mechanical hands today, manipulating objects remains one of the toughest challenges in robotics. Bruce Welty, founder and chairman of Locus Robotics, which makes wheeled warehouse robots that carry items picked by humans, says attending Amazon's previous robot contests was both inspiring and humbling.
A Knightscope K5 security robot roamed the Prudential Center in Boston in May. A Knightscope K5 security robot roamed the Prudential Center in Boston in May. Some of the best minds of our times, including Stephen Hawking and Elon Musk, have warned that human beings may invent intelligent machines that could wind up destroying humankind. A Knightscope K5 security robot that patrolled an office complex along the Georgetown waterfront in Washington, D.C., rolled itself into a shallow fountain on Monday -- and drowned.
Over the last several years, a team of roboticists at the University of Tehran has been working on increasingly large and complex life-size humanoids. A team of 15 researchers at University of Tehran's Center for Advanced Systems and Technologies worked for over a year to design and build Surena Mini, which is 50 centimeters tall and weighs 3.4 kilograms. Its hands aren't designed for grasping objects, but Surena Mini can push on small things--or karate-chop them: A little over a year ago, the same group unveiled Surena III, an advanced adult-size humanoid designed for researching bipedal locomotion, human-robot interaction, and other challenges in robotics. The Iranian roboticists plan to continue working on Surena III, but they also want to explore the possibility of creating marketable products based on their research, Professor Yousefi-Koma explained, and one of the ideas they had was building a "kid-size version of Surena."
In the view of electronic design automation firms, machine learning tools could chisel rough edges off complex chips, improving productivity, optimizing trade-offs like power consumption and timing, and testing that chips are ready for manufacturing. The firm recently released new characterization tools, which after being trained on circuit simulations can make faster predictions than other tools about how, for instance, standard cells and memory will react to higher-than-normal voltages. To learn how machine learning fits into chip design is the mission of CAEML, which launched last year with National Science Foundation support. Sorin Dobre, senior technology director and manager for Qualcomm, who has designed digital chips from 180nm to 7nm, said that such tools could not only make life easier for senior engineers but also make chip design more accessible to those without decades of experience.
He is also an Affiliate Associate Professor in Department of Electrical and Computer Engineering at Concordia University, Canada. Prior to joining Enjoyor Inc. in 2012, he held positions with Huawei Technologies, the China Academy of Telecommunication Technology, the Chinese University of Hong Kong, the Hong Kong University of Science and Technology, and Concordia University. His current research interests include signal processing, neural networks, intelligent systems, and wireless communications. Swamy is currently a Research Professor and holder of the Concordia Tier I Research Chair Signal Processing in the Department of Electrical and Computer Engineering, Concordia University, where he was Dean of the Faculty of Engineering and Computer Science from 1977 to 1993 and the founding Chair of the EE department.