This is a guest post. The views expressed here are solely those of the authors and do not represent positions of IEEE Spectrum or the IEEE. Despite decades of expectations that we will have dexterous robots performing sophisticated tasks in the house and elsewhere, the use of robots remains painfully limited, largely due to insufficient motion-planning performance. Motion planning is the process of determining how to move a robot, or autonomous vehicle, from its current configuration (or pose) to a desired goal configuration: For example, how to reach into a fridge to grab a soda can while avoiding obstacles, like the other items in the fridge and the fridge itself. Until recently, this critical process has been implemented in software running on high-performance commodity hardware.
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. The bar has now been set for robot holiday videos, thanks to FZI. Still waiting for a robot with a cookie to show up at my door.
Quadrotors are fast, cheap, and capable, and they're getting smarter all the time. Where they struggle a little bit is with adaptation. Many other kinds of robots can change their structure to better perform different tasks: Humanoids do it all the time, with all those conveniently placed limbs. Hey, wouldn't it be cool if drones had movable limbs too? Someone should figure out how to do that.
RHex (pronounced "rex") is a unique hexapedal robot that uses hybrid wheel-legs (whegs) to get around. It's surprisingly adaptable, able to adjust its gait to conquer a variety of obstacles and terrains, and it can even do some impressive parkour. RHex has been around for nearly two decades, which is practically forever in robot years, but because of how versatile it is you still see it doing cool new stuff from time to time. Carnegie Mellon University's Robomechanics Lab uses a fancy US $20,000 version of RHex called X-RHex Lite "to explore the connection between dynamic locomotion and perception," but they've only got one robot since it's wicked expensive, which limits the amount of research and outreach they can do. To fix this, they've designed a much smaller version of RHex called MiniRHex that you can build yourself for about $200.
What's the best type of device from which to build a neural network? Of course, it should be fast, small, consume little power, have the ability to reliably store many bits-worth of information. And if it's going to be involved in learning new tricks as well as performing those tricks, it has to behave predictably during the learning process. Neural networks can be thought of as a group of cells connected to other cells. These connections--synapses in biological neurons--all have particular strengths, or weights, associated with them.
Welcome to the seventh edition of IEEE Spectrum's Robot Gift Guide! Our apologies for being a bit late with our list this year (too many projects and trips!), but we hope it'll help you find the best giftable robots for your family, friends, and other special people in your life, including yourself, of course. As in previous years, we tried to include a wide variety of robot types and prices, focusing mostly on products released this year. If you need even more robot gift ideas, take a look at our past guides: 2017, 2016, 2015, 2014, 2013, and 2012. Some of those robots are still great choices and are probably way cheaper now than when we first posted about them.
Metamaterials seem like a technology out of science fiction. Because of the way these materials affect electromagnetic phenomena and physical attributes of materials, they can render objects invisible, leaving the observer in disbelief. While invisibility cloaks are a gee-whiz application, metamaterials now offer real-world commercial applications such as new antenna technologies for mobile phones. To get to the point where metamaterials are not just a curiosity, but also a viable commercial technology, they have had to evolve a new set of tricks . One example is the work of a team of researchers from Lawrence Livermore National Laboratory (LLNL) and the University of California San Diego (UCSD).
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. ESA astronaut Alexander Gerst welcomed a new face to the Columbus laboratory, thanks to the successful commissioning of technology demonstration Cimon. Short for Crew Interactive Mobile CompanioN, Cimon is a 3D-printed plastic sphere designed to test human-machine interaction in space.
Unmanned aerial vehicles (UAVs), more commonly known as drones, are growing at a rapid rate for both consumer and professional markets. Market research firm IHS Markit forecasts the professional drone market will manage a compound annual growth rate (CAGR) of 77.1% through 2020 driven by industries such as agriculture, energy and construction using the technology for surveying, mapping, planning and more. Meanwhile, the consumer drone market will maintain a CAGR of 22.1% through 2020 with companies such as DJI, Parrot and 3D Robotics driving the market with a wide range of devices for photography, recreational use and racing. While these markets will be the main drivers for the next few years, one industry that isn't discussed often as a main driver is the insurance market. However, according to professional services company PwC, the addressable market of drone powered solutions in the insurance industry is valued at $6.8 billion.
DeepMind, the London-based subsidiary of Alphabet, has created a system that can quickly master any game in the class that includes chess, Go, and Shogi, and do so without human guidance. The system, called AlphaZero, began its life last year by beating a DeepMind system that had been specialized just for Go. That earlier system had itself made history by beating one of the world's best Go players, but it needed human help to get through a months-long course of improvement. AlphaZero trained itself--in just 3 days. AlphaZero, playing White against Stockfish, began by identifying four candidate moves.