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#435791 To Fly Solo, Racing Drones Have a Need ...
Drone racing’s ultimate vision of quadcopters weaving nimbly through obstacle courses has attracted far less excitement and investment than self-driving cars aimed at reshaping ground transportation. But the U.S. military and defense industry are betting on autonomous drone racing as the next frontier for developing AI so that it can handle high-speed navigation within tight spaces without human intervention.
The autonomous drone challenge requires split-second decision-making with six degrees of freedom instead of a car’s mere two degrees of road freedom. One research team developing the AI necessary for controlling autonomous racing drones is the Robotics and Perception Group at the University of Zurich in Switzerland. In late May, the Swiss researchers were among nine teams revealed to be competing in the two-year AlphaPilot open innovation challenge sponsored by U.S. aerospace company Lockheed Martin. The winning team will walk away with up to $2.25 million for beating other autonomous racing drones and a professional human drone pilot in head-to-head competitions.
“I think it is important to first point out that having an autonomous drone to finish a racing track at high speeds or even beating a human pilot does not imply that we can have autonomous drones [capable of] navigating in real-world, complex, unstructured, unknown environments such as disaster zones, collapsed buildings, caves, tunnels or narrow pipes, forests, military scenarios, and so on,” says Davide Scaramuzza, a professor of robotics and perception at the University of Zurich and ETH Zurich. “However, the robust and computationally efficient state estimation algorithms, control, and planning algorithms developed for autonomous drone racing would represent a starting point.”
The nine teams that made the cut—from a pool of 424 AlphaPilot applicants—will compete in four 2019 racing events organized under the Drone Racing League’s Artificial Intelligence Robotic Racing Circuit, says Keith Lynn, program manager for AlphaPilot at Lockheed Martin. To ensure an apples-to-apples comparison of each team’s AI secret sauce, each AlphaPilot team will upload its AI code into identical, specially-built drones that have the NVIDIA Xavier GPU at the core of the onboard computing hardware.
“Lockheed Martin is offering mentorship to the nine AlphaPilot teams to support their AI tech development and innovations,” says Lynn. The company “will be hosting a week-long Developers Summit at MIT in July, dedicated to workshopping and improving AlphaPilot teams’ code,” he added. He notes that each team will retain the intellectual property rights to its AI code.
The AlphaPilot challenge takes inspiration from older autonomous drone racing events hosted by academic researchers, Scaramuzza says. He credits Hyungpil Moon, a professor of robotics and mechanical engineering at Sungkyunkwan University in South Korea, for having organized the annual autonomous drone racing competition at the International Conference on Intelligent Robots and Systems since 2016.
It’s no easy task to create and train AI that can perform high-speed flight through complex environments by relying on visual navigation. One big challenge comes from how drones can accelerate sharply, take sharp turns, fly sideways, do zig-zag patterns and even perform back flips. That means camera images can suddenly appear tilted or even upside down during drone flight. Motion blur may occur when a drone flies very close to structures at high speeds and camera pixels collect light from multiple directions. Both cameras and visual software can also struggle to compensate for sudden changes between light and dark parts of an environment.
To lend AI a helping hand, Scaramuzza’s group recently published a drone racing dataset that includes realistic training data taken from a drone flown by a professional pilot in both indoor and outdoor spaces. The data, which includes complicated aerial maneuvers such as back flips, flight sequences that cover hundreds of meters, and flight speeds of up to 83 kilometers per hour, was presented at the 2019 IEEE International Conference on Robotics and Automation.
The drone racing dataset also includes data captured by the group’s special bioinspired event cameras that can detect changes in motion on a per-pixel basis within microseconds. By comparison, ordinary cameras need milliseconds (each millisecond being 1,000 microseconds) to compare motion changes in each image frame. The event cameras have already proven capable of helping drones nimbly dodge soccer balls thrown at them by the Swiss lab’s researchers.
The Swiss group’s work on the racing drone dataset received funding in part from the U.S. Defense Advanced Research Projects Agency (DARPA), which acts as the U.S. military’s special R&D arm for more futuristic projects. Specifically, the funding came from DARPA’s Fast Lightweight Autonomy program that envisions small autonomous drones capable of flying at high speeds through cluttered environments without GPS guidance or communication with human pilots.
Such speedy drones could serve as military scouts checking out dangerous buildings or alleys. They could also someday help search-and-rescue teams find people trapped in semi-collapsed buildings or lost in the woods. Being able to fly at high speed without crashing into things also makes a drone more efficient at all sorts of tasks by making the most of limited battery life, Scaramuzza says. After all, most drone battery life gets used up by the need to hover in flight and doesn’t get drained much by flying faster.
Even if AI manages to conquer the drone racing obstacle courses, that would be the end of the beginning of the technology’s development. What would still be required? Scaramuzza specifically singled out the need to handle low-visibility conditions involving smoke, dust, fog, rain, snow, fire, hail, as some of the biggest challenges for vision-based algorithms and AI in complex real-life environments.
“I think we should develop and release datasets containing smoke, dust, fog, rain, fire, etc. if we want to allow using autonomous robots to complement human rescuers in saving people lives after an earthquake or natural disaster in the future,” Scaramuzza says. Continue reading →
Posted in Human Robots
#435726 This Is the Most Powerful Robot Arm Ever ...
Last month, engineers at NASA’s Jet Propulsion Laboratory wrapped up the installation of the Mars 2020 rover’s 2.1-meter-long robot arm. This is the most powerful arm ever installed on a Mars rover. Even though the Mars 2020 rover shares much of its design with Curiosity, the new arm was redesigned to be able to do much more complex science, drilling into rocks to collect samples that can be stored for later recovery.
JPL is well known for developing robots that do amazing work in incredibly distant and hostile environments. The Opportunity Mars rover, to name just one example, had a 90-day planned mission but remained operational for 5,498 days in a robot unfriendly place full of dust and wild temperature swings where even the most basic maintenance or repair is utterly impossible. (Its twin rover, Spirit, operated for 2,269 days.)
To learn more about the process behind designing robotic systems that are capable of feats like these, we talked with Matt Robinson, one of the engineers who designed the Mars 2020 rover’s new robot arm.
The Mars 2020 rover (which will be officially named through a public contest which opens this fall) is scheduled to launch in July of 2020, landing in Jezero Crater on February 18, 2021. The overall design is similar to the Mars Science Laboratory (MSL) rover, named Curiosity, which has been exploring Gale Crater on Mars since August 2012, except Mars 2020 will be a bit bigger and capable of doing even more amazing science. It will outweigh Curiosity by about 150 kilograms, but it’s otherwise about the same size, and uses the same type of radioisotope thermoelectric generator for power. Upgraded aluminum wheels will be more durable than Curiosity’s wheels, which have suffered significant wear. Mars 2020 will land on Mars in the same way that Curiosity did, with a mildly insane descent to the surface from a rocket-powered hovering “skycrane.”
Photo: NASA/JPL-Caltech
Last month, engineers at NASA's Jet Propulsion Laboratory install the main robotic arm on the Mars 2020 rover. Measuring 2.1 meters long, the arm will allow the rover to work as a human geologist would: by holding and using science tools with its turret.
Mars 2020 really steps it up when it comes to science. The most interesting new capability (besides serving as the base station for a highly experimental autonomous helicopter) is that the rover will be able to take surface samples of rock and soil, put them into tubes, seal the tubes up, and then cache the tubes on the surface for later retrieval (and potentially return to Earth for analysis). Collecting the samples is the job of a drill on the end of the robot arm that can be equipped with a variety of interchangeable bits, but the arm holds a number of other instruments as well. A “turret” can swap between the drill, a mineral identification sensor suite called SHERLOC, and an X-ray spectrometer and camera called PIXL. Fundamentally, most of Mars 2020’s science work is going to depend on the arm and the hardware that it carries, both in terms of close-up surface investigations and collecting samples for caching.
Matt Robinson is the Deputy Delivery Manager for the Sample Caching System on the Mars 2020 rover, which covers the robotic arm itself, the drill at the end of the arm, and the sample caching system within the body of the rover that manages the samples. Robinson has been at JPL since 2001, and he’s worked on the Mars Phoenix Lander mission as the robotic arm flight software developer and robotic arm test and operations engineer, as well as on Curiosity as the robotic arm test and operations lead engineer.
We spoke with Robinson about how the Mars 2020 arm was designed, and what it’s like to be building robots for exploring other planets.
IEEE Spectrum: How’d you end up working on robots at JPL?
Matt Robinson: When I was a grad student, my focus was on vision-based robotics research, so the kinds of things they do at JPL, or that we do at JPL now, were right within my wheelhouse. One of my advisors in grad school had a former student who was out here at JPL, so that’s how I made the contact. But I was very excited to come to JPL—as a young grad student working in robotics, space robotics was where it’s at.
For a robotics engineer, working in space is kind of the gold standard. You’re working in a challenging environment and you have to be prepared for any time of eventuality that may occur. And when you send your robot out to space, there’s no getting it back.
Once the rover arrives on Mars and you receive pictures back from it operating, there’s no greater feeling. You’ve built something that is now working 200+ million miles away. It’s an awesome experience! I have to pinch myself sometimes with the job I do. Working at JPL on space robotics is the holy grail for a roboticist.
What’s different about designing an arm for a rover that will operate on Mars?
We spent over five years designing, manufacturing, assembling, and testing the arm. Scientists have defined the high-level goals for what the mission has to do—acquire core samples and process them for return, carry science instruments on the arm to help determine what rocks to sample, and so on. We, as engineers, define the next level of requirements that support those goals.
When you’re building a robotic arm for another planet, you want to design something that is robust to the environment as well as robust from fault-protection standpoint. On Mars, we’re talking about an environment where the temperature can vary 100 degrees Celsius over the course of the day, so it’s very challenging thermally. With force sensing for instance, that’s a major problem. Force sensors aren’t typically designed to operate or even survive in temperature ranges that we’re talking about. So a lot of effort has to go into force sensor design and testing.
And then there’s a do-no-harm aspect—you’re sending this piece of hardware 200 million miles away, and you can’t get it back, so you want to make sure your hardware and software are robust and cannot do any harm to the system. It’s definitely a change in mindset from a terrestrial robot, where if you make a mistake, you can repair it.
“Once the rover arrives on Mars and you receive pictures back from it, there’s no greater feeling . . . I have to pinch myself sometimes with the job I do.”
—Matt Robinson, NASA JPL
How do you decide how much redundancy is enough?
That’s always a big question. It comes down to a couple of things, typically: mass and volume. You have a certain amount of mass that’s allocated to the robotic arm and we have a volume that it has to fit within, so those are often the drivers of the amount of redundancy that you can fit. We also have a lot of experience with sending arms to other planets, and at the beginning of projects, we establish a number of requirements that the design has to meet, and that’s where the redundancy is captured.
How much is the design of the arm driven by this need for redundancy, as opposed to trying to pack in all of the instrumentation that you want to have on there to do as much science as possible?
The requirements were driven by a couple of things. We knew roughly how big the instruments on the end of the arm were going to be, so the arm design is partially driven by that, because as the instruments get bigger and heavier, the arm has to get bigger and stronger. We have our coring drill at the end of the arm, and coring requires a certain level of force, so the arm has to be strong enough to do that. Those all became requirements that drove the design of the arm. On top of that, there was also that this arm also has to operate within the Martian environment, so you have things like the temperature changes and thermal expansion—you have to design for that as well. It’s a combination of both, really.
You were a test engineer for the arm used on the MSL rover. What did you learn from Spirit and Opportunity that informed the design of the arm on Curiosity?
Spirit and Opportunity did not have any force-sensing on the robotic arm. We had contact sensors that were good enough. Spirit and Opportunity’s arms were used to place instruments, that’s all it had to do, primarily. When you’re talking about actually acquiring samples, it’s not a matter of just placing the tool—you also have to apply forces to the environment. And once you start doing that, you really need a force sensor to protect you, and also to determine how much load to apply. So that was a big theme, a big difference between MSL and Spirit and Opportunity.
The size grew a lot too. If you look at Spirit and Opportunity, they’re the size of a riding lawnmower. Curiosity and the Mars 2020 rovers are the size of a small car. The Spirit and Opportunity arm was under a meter long, and the 2020 arm is twice that, and it has to apply forces that are much higher than the Spirit and Opportunity arm. From Curiosity to 2020, the payload of the arm grew by 50 percent, but the mass of the arm did not grow a whole lot, because our mass budget was kind of tight. We had to design an arm that was stronger, that had more capability, without adding more mass. That was a big challenge. We were fairly efficient on Curiosity, but on 2020, we sharpened the pencil even more.
Photo: NASA/JPL-Caltech
Three generations of Mars rovers developed at NASA’s Jet Propulsion Laboratory. Front and center: Sojourner rover, which landed on Mars in 1997 as part of the Mars Pathfinder Project. Left: Mars Exploration Rover Project rover (Spirit and Opportunity), which landed on Mars in 2004. Right: Mars Science Laboratory rover (Curiosity), which landed on Mars in August 2012.
MSL used its arm to drill into rocks like Mars 2020 will—how has the experience of operating MSL on Mars changed your thinking on how to make that work?
On MSL, the force sensor was used primarily for fault protection, just to protect the arm from being overloaded. [When drilling] we used a stiffness model of the arm to apply the force. The force sensor was only used in case you overloaded, and that’s very different from doing active force control, where you’re actually using the force sensor in a control loop.
On Mars 2020, we’re taking it to the next step, using the force sensor to actually actively control the level of force, both for pushing on the ground and for doing bit exchange. That’s a key point because fault protection to prevent damage usually has larger error bars. When you’re trying to actually push on the environment to apply force, and you’re doing active force control, the force sensor has to be significantly more accurate.
So a big thing that we learned on MSL—it was the first time we’d actually flown a force sensor, and we learned a lot about how to design and test force sensors to be used on the surface of Mars.
How do you effectively test the Mars 2020 arm on Earth?
That’s a good question. The arm was designed to operate on either Earth or Mars. It’s strong enough to do both. We also have a stiffness model of the arm which includes allows us to compensate for differences in gravity. For testing, we make two copies of the robotic arm. We have our copy that we’re going to fly to Mars, which is what we call our flight model, and we have our engineering model. They’re effectively duplicates of each other. The engineering arm stays on earth, so even once we’ve sent the flight model to Mars, we can continue to test. And if something were to happen, if say a drill bit got stuck in the ground on Mars, we could try to replicate those conditions on Earth with our engineering model arm, and use that to test out different scenarios to overcome the problem.
How much autonomy will the arm have?
We have different models of autonomy. We have pretty high levels flight software and, for instance, we have a command that just says “dock,” that moves the arm does all the force control to the dock the arm with the carousel. For surface interaction, we have stereo cameras on the rover, and those cameras allow us to generate 3D terrain models. Using those 3D terrain models, scientists can select a target on that surface, and then we can position the arm on the target.
Scientists like to select the particular sample targets, because they have very specific types of rocks they’re looking for to sample from. On 2020, we’re providing the ability for the next level of autonomy for the rover to drive up to an area and at least do the initial surveying of that area, so the scientists can select the specific target. So the way that that would happen is, if there’s an area off in the distance that the scientists find potentially interesting, the rover will autonomously drive up to it, and deploy the arm and take all the pictures so that we can generate those 3D terrain models and then the next day the scientists can pick the specific target they want. It’s really cool.
JPL is famous for making robots that operate for far longer than NASA necessarily plans for. What’s it like designing hardware and software for a system that will (hopefully) become part of that legacy?
The way that I look at it is, when you’re building an arm that’s going to go to another planet, all the things that could go wrong… You have to build something that’s robust and that can survive all that. It’s not that we’re trying to overdesign arms so that they’ll end up lasting much, much longer, it’s that, given all the things that you can encounter within a fairly unknown environment, and the level of robustness of the design you have to apply, it just so happens we end up with designs that end up lasting a lot longer than they do. Which is great, but we’re not held to that, although we’re very excited when we see them last that long. Without any calibration, without any maintenance, exactly, it’s amazing. They show their wear over time, but they still operate, it’s super exciting, it’s very inspirational to see.
[ Mars 2020 Rover ] Continue reading →
Posted in Human Robots
#435658 Video Friday: A Two-Armed Robot That ...
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!):
ICRES 2019 – July 29-30, 2019 – London, U.K.
DARPA SubT Tunnel Circuit – August 15-22, 2019 – Pittsburgh, Pa., USA
IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
Let us know if you have suggestions for next week, and enjoy today’s videos.
I’m sure you’ve seen this video already because you read this blog every day, but if you somehow missed it because you were skiing across Antarctica (the only valid excuse we’re accepting today), here’s our video introducing HMI’s Aquanaut transforming robot submarine.
And after you recover from all that frostbite, make sure and read our in-depth feature article here.
[ Aquanaut ]
Last week we complained about not having seen a ballbot with a manipulator, so Roberto from CMU shared a new video of their ballbot, featuring a pair of 7-DoF arms.
We should learn more at Humanoids 2019.
[ CMU ]
Thanks Roberto!
The FAA is making it easier for recreational drone pilots to get near-realtime approval to fly in lightly controlled airspace.
[ LAANC ]
Self-reconfigurable modular robots are usually composed of multiple modules with uniform docking interfaces that can be transformed into different configurations by themselves. The reconfiguration planning problem is finding what sequence of reconfiguration actions are required for one arrangement of modules to transform into another. We present a novel reconfiguration planning algorithm for modular robots. The algorithm compares the initial configuration with the goal configuration efficiently. The reconfiguration actions can be executed in a distributed manner so that each module can efficiently finish its reconfiguration task which results in a global reconfiguration for the system. In the end, the algorithm is demonstrated on real modular robots and some example reconfiguration tasks are provided.
[ CKbot ]
A nice design of a gripper that uses a passive thumb of sorts to pick up flat objects from flat surfaces.
[ Paper ] via [ Laval University ]
I like this video of a palletizing robot from Kawasaki because in the background you can see a human doing the exact same job and obviously not enjoying it.
[ Kawasaki ]
This robot cleans and “brings joy and laughter.” What else do we need?
I do appreciate that all the robots are named Leo, and that they’re also all female.
[ LionsBot ]
This is less of a dishwashing robot and more of a dishsorting robot, but we’ll forgive it because it doesn’t drop a single dish.
[ TechMagic ]
Thanks Ryosuke!
A slight warning here that the robot in the following video (which costs something like $180,000) appears “naked” in some scenes, none of which are strictly objectionable, we hope.
Beautifully slim and delicate motion life-size motion figures are ideal avatars for expressing emotions to customers in various arts, content and businesses. We can provide a system that integrates not only motion figures but all moving devices.
[ Speecys ]
The best way to operate a Husky with a pair of manipulators on it is to become the robot.
[ UT Austin ]
The FlyJacket drone control system from EPFL has been upgraded so that it can yank you around a little bit.
In several fields of human-machine interaction, haptic guidance has proven to be an effective training tool for enhancing user performance. This work presents the results of psychophysical and motor learning studies that were carried out with human participant to assess the effect of cable-driven haptic guidance for a task involving aerial robotic teleoperation. The guidance system was integrated into an exosuit, called the FlyJacket, that was developed to control drones with torso movements. Results for the Just Noticeable Difference (JND) and from the Stevens Power Law suggest that the perception of force on the users’ torso scales linearly with the amplitude of the force exerted through the cables and the perceived force is close to the magnitude of the stimulus. Motor learning studies reveal that this form of haptic guidance improves user performance in training, but this improvement is not retained when participants are evaluated without guidance.
[ EPFL ]
The SAND Challenge is an opportunity for small businesses to compete in an autonomous unmanned aerial vehicle (UAV) competition to help NASA address safety-critical risks associated with flying UAVs in the national airspace. Set in a post-natural disaster scenario, SAND will push the envelope of aviation.
[ NASA ]
Legged robots have the potential to traverse diverse and rugged terrain. To find a safe and efficient navigation path and to carefully select individual footholds, it is useful to predict properties of the terrain ahead of the robot. In this work, we propose a method to collect data from robot-terrain interaction and associate it to images, to then train a neural network to predict terrain properties from images.
[ RSL ]
Misty wants to be your new receptionist.
[ Misty Robotics ]
For years, we’ve been pointing out that while new Roombas have lots of great features, older Roombas still do a totally decent job of cleaning your floors. This video is a performance comparison between the newest Roomba (the S9+) and the original 2002 Roomba (!), and the results will surprise you. Or maybe they won’t.
[ Vacuum Wars ]
Lex Fridman from MIT interviews Chris Urmson, who was involved in some of the earliest autonomous vehicle projects, Google’s original self-driving car among them, and is currently CEO of Aurora Innovation.
Chris Urmson was the CTO of the Google Self-Driving Car team, a key engineer and leader behind the Carnegie Mellon autonomous vehicle entries in the DARPA grand challenges and the winner of the DARPA urban challenge. Today he is the CEO of Aurora Innovation, an autonomous vehicle software company he started with Sterling Anderson, who was the former director of Tesla Autopilot, and Drew Bagnell, Uber’s former autonomy and perception lead.
[ AI Podcast ]
In this week’s episode of Robots in Depth, Per speaks with Lael Odhner from RightHand Robotics.
Lael Odhner is a co-founder of RightHand Robotics, that is developing a gripper based on the combination of control and soft, compliant parts to get better grasping of objects. Their work focuses on grasping and manipulating everyday human objects in everyday environments.This mimics how human hands combine control and flexibility to grasp objects with great dexterity.
The combination of control and compliance makes the RightHand robotics gripper very light-weight and affordable. The compliance makes it easier to grasp objects of unknown shape and differs from the way industrial robots usually grip. The compliance also helps in a more unstructured environment where contact with the object and its surroundings cannot be exactly predicted.
[ RightHand Robotics ] via [ Robots in Depth ] Continue reading →
Posted in Human Robots
#435589 Construction Robots Learn to Excavate by ...
Pavel Savkin remembers the first time he watched a robot imitate his movements. Minutes earlier, the engineer had finished “showing” the robotic excavator its new goal by directing its movements manually. Now, running on software Savkin helped design, the robot was reproducing his movements, gesture for gesture. “It was like there was something alive in there—but I knew it was me,” he said.
Savkin is the CTO of SE4, a robotics software project that styles itself the “driver” of a fleet of robots that will eventually build human colonies in space. For now, SE4 is focused on creating software that can help developers communicate with robots, rather than on building hardware of its own.
The Tokyo-based startup showed off an industrial arm from Universal Robots that was running SE4’s proprietary software at SIGGRAPH in July. SE4’s demonstration at the Los Angeles innovation conference drew the company’s largest audience yet. The robot, nicknamed Squeezie, stacked real blocks as directed by SE4 research engineer Nathan Quinn, who wore a VR headset and used handheld controls to “show” Squeezie what to do.
As Quinn manipulated blocks in a virtual 3D space, the software learned a set of ordered instructions to be carried out in the real world. That order is essential for remote operations, says Quinn. To build remotely, developers need a way to communicate instructions to robotic builders on location. In the age of digital construction and industrial robotics, giving a computer a blueprint for what to build is a well-explored art. But operating on a distant object—especially under conditions that humans haven’t experienced themselves—presents challenges that only real-time communication with operators can solve.
The problem is that, in an unpredictable setting, even simple tasks require not only instruction from an operator, but constant feedback from the changing environment. Five years ago, the Swedish fiber network provider umea.net (part of the private Umeå Energy utility) took advantage of the virtual reality boom to promote its high-speed connections with the help of a viral video titled “Living with Lag: An Oculus Rift Experiment.” The video is still circulated in VR and gaming circles.
In the experiment, volunteers donned headgear that replaced their real-time biological senses of sight and sound with camera and audio feeds of their surroundings—both set at a 3-second delay. Thus equipped, volunteers attempt to complete everyday tasks like playing ping-pong, dancing, cooking, and walking on a beach, with decidedly slapstick results.
At outer-orbit intervals, including SE4’s dream of construction projects on Mars, the limiting factor in communication speed is not an artificial delay, but the laws of physics. The shifting relative positions of Earth and Mars mean that communications between the planets—even at the speed of light—can take anywhere from 3 to 22 minutes.
A long-distance relationship
Imagine trying to manage a construction project from across an ocean without the benefit of intelligent workers: sending a ship to an unknown world with a construction crew and blueprints for a log cabin, and four months later receiving a letter back asking how to cut down a tree. The parallel problem in long-distance construction with robots, according to SE4 CEO Lochlainn Wilson, is that automation relies on predictability. “Every robot in an industrial setting today is expecting a controlled environment.”
Platforms for applying AR and VR systems to teach tasks to artificial intelligences, as SE4 does, are already proliferating in manufacturing, healthcare, and defense. But all of the related communications systems are bound by physics and, specifically, the speed of light.
The same fundamental limitation applies in space. “Our communications are light-based, whether they’re radio or optical,” says Laura Seward Forczyk, a planetary scientist and consultant for space startups. “If you’re going to Mars and you want to communicate with your robot or spacecraft there, you need to have it act semi- or mostly-independently so that it can operate without commands from Earth.”
Semantic control
That’s exactly what SE4 aims to do. By teaching robots to group micro-movements into logical units—like all the steps to building a tower of blocks—the Tokyo-based startup lets robots make simple relational judgments that would allow them to receive a full set of instruction modules at once and carry them out in order. This sidesteps the latency issue in real-time bilateral communications that could hamstring a project or at least make progress excruciatingly slow.
The key to the platform, says Wilson, is the team’s proprietary operating software, “Semantic Control.” Just as in linguistics and philosophy, “semantics” refers to meaning itself, and meaning is the key to a robot’s ability to make even the smallest decisions on its own. “A robot can scan its environment and give [raw data] to us, but it can’t necessarily identify the objects around it and what they mean,” says Wilson.
That’s where human intelligence comes in. As part of the demonstration phase, the human operator of an SE4-controlled machine “annotates” each object in the robot’s vicinity with meaning. By labeling objects in the VR space with useful information—like which objects are building material and which are rocks—the operator helps the robot make sense of its real 3D environment before the building begins.
Giving robots the tools to deal with a changing environment is an important step toward allowing the AI to be truly independent, but it’s only an initial step. “We’re not letting it do absolutely everything,” said Quinn. “Our robot is good at moving an object from point A to point B, but it doesn’t know the overall plan.” Wilson adds that delegating environmental awareness and raw mechanical power to separate agents is the optimal relationship for a mixed human-robot construction team; it “lets humans do what they’re good at, while robots do what they do best.”
This story was updated on 4 September 2019. Continue reading →
Posted in Human Robots
#434781 What Would It Mean for AI to Become ...
As artificial intelligence systems take on more tasks and solve more problems, it’s hard to say which is rising faster: our interest in them or our fear of them. Futurist Ray Kurzweil famously predicted that “By 2029, computers will have emotional intelligence and be convincing as people.”
We don’t know how accurate this prediction will turn out to be. Even if it takes more than 10 years, though, is it really possible for machines to become conscious? If the machines Kurzweil describes say they’re conscious, does that mean they actually are?
Perhaps a more relevant question at this juncture is: what is consciousness, and how do we replicate it if we don’t understand it?
In a panel discussion at South By Southwest titled “How AI Will Design the Human Future,” experts from academia and industry discussed these questions and more.
Wait, What Is AI?
Most of AI’s recent feats—diagnosing illnesses, participating in debate, writing realistic text—involve machine learning, which uses statistics to find patterns in large datasets then uses those patterns to make predictions. However, “AI” has been used to refer to everything from basic software automation and algorithms to advanced machine learning and deep learning.
“The term ‘artificial intelligence’ is thrown around constantly and often incorrectly,” said Jennifer Strong, a reporter at the Wall Street Journal and host of the podcast “The Future of Everything.” Indeed, one study found that 40 percent of European companies that claim to be working on or using AI don’t actually use it at all.
Dr. Peter Stone, associate chair of computer science at UT Austin, was the study panel chair on the 2016 One Hundred Year Study on Artificial Intelligence (or AI100) report. Based out of Stanford University, AI100 is studying and anticipating how AI will impact our work, our cities, and our lives.
“One of the first things we had to do was define AI,” Stone said. They defined it as a collection of different technologies inspired by the human brain to be able to perceive their surrounding environment and figure out what actions to take given these inputs.
Modeling on the Unknown
Here’s the crazy thing about that definition (and about AI itself): we’re essentially trying to re-create the abilities of the human brain without having anything close to a thorough understanding of how the human brain works.
“We’re starting to pair our brains with computers, but brains don’t understand computers and computers don’t understand brains,” Stone said. Dr. Heather Berlin, cognitive neuroscientist and professor of psychiatry at the Icahn School of Medicine at Mount Sinai, agreed. “It’s still one of the greatest mysteries how this three-pound piece of matter can give us all our subjective experiences, thoughts, and emotions,” she said.
This isn’t to say we’re not making progress; there have been significant neuroscience breakthroughs in recent years. “This has been the stuff of science fiction for a long time, but now there’s active work being done in this area,” said Amir Husain, CEO and founder of Austin-based AI company Spark Cognition.
Advances in brain-machine interfaces show just how much more we understand the brain now than we did even a few years ago. Neural implants are being used to restore communication or movement capabilities in people who’ve been impaired by injury or illness. Scientists have been able to transfer signals from the brain to prosthetic limbs and stimulate specific circuits in the brain to treat conditions like Parkinson’s, PTSD, and depression.
But much of the brain’s inner workings remain a deep, dark mystery—one that will have to be further solved if we’re ever to get from narrow AI, which refers to systems that can perform specific tasks and is where the technology stands today, to artificial general intelligence, or systems that possess the same intelligence level and learning capabilities as humans.
The biggest question that arises here, and one that’s become a popular theme across stories and films, is if machines achieve human-level general intelligence, does that also mean they’d be conscious?
Wait, What Is Consciousness?
As valuable as the knowledge we’ve accumulated about the brain is, it seems like nothing more than a collection of disparate facts when we try to put it all together to understand consciousness.
“If you can replace one neuron with a silicon chip that can do the same function, then replace another neuron, and another—at what point are you still you?” Berlin asked. “These systems will be able to pass the Turing test, so we’re going to need another concept of how to measure consciousness.”
Is consciousness a measurable phenomenon, though? Rather than progressing by degrees or moving through some gray area, isn’t it pretty black and white—a being is either conscious or it isn’t?
This may be an outmoded way of thinking, according to Berlin. “It used to be that only philosophers could study consciousness, but now we can study it from a scientific perspective,” she said. “We can measure changes in neural pathways. It’s subjective, but depends on reportability.”
She described three levels of consciousness: pure subjective experience (“Look, the sky is blue”), awareness of one’s own subjective experience (“Oh, it’s me that’s seeing the blue sky”), and relating one subjective experience to another (“The blue sky reminds me of a blue ocean”).
“These subjective states exist all the way down the animal kingdom. As humans we have a sense of self that gives us another depth to that experience, but it’s not necessary for pure sensation,” Berlin said.
Husain took this definition a few steps farther. “It’s this self-awareness, this idea that I exist separate from everything else and that I can model myself,” he said. “Human brains have a wonderful simulator. They can propose a course of action virtually, in their minds, and see how things play out. The ability to include yourself as an actor means you’re running a computation on the idea of yourself.”
Most of the decisions we make involve envisioning different outcomes, thinking about how each outcome would affect us, and choosing which outcome we’d most prefer.
“Complex tasks you want to achieve in the world are tied to your ability to foresee the future, at least based on some mental model,” Husain said. “With that view, I as an AI practitioner don’t see a problem implementing that type of consciousness.”
Moving Forward Cautiously (But Not too Cautiously)
To be clear, we’re nowhere near machines achieving artificial general intelligence or consciousness, and whether a “conscious machine” is possible—not to mention necessary or desirable—is still very much up for debate.
As machine intelligence continues to advance, though, we’ll need to walk the line between progress and risk management carefully.
Improving the transparency and explainability of AI systems is one crucial goal AI developers and researchers are zeroing in on. Especially in applications that could mean the difference between life and death, AI shouldn’t advance without people being able to trace how it’s making decisions and reaching conclusions.
Medicine is a prime example. “There are already advances that could save lives, but they’re not being used because they’re not trusted by doctors and nurses,” said Stone. “We need to make sure there’s transparency.” Demanding too much transparency would also be a mistake, though, because it will hinder the development of systems that could at best save lives and at worst improve efficiency and free up doctors to have more face time with patients.
Similarly, self-driving cars have great potential to reduce deaths from traffic fatalities. But even though humans cause thousands of deadly crashes every day, we’re terrified by the idea of self-driving cars that are anything less than perfect. “If we only accept autonomous cars when there’s zero probability of an accident, then we will never accept them,” Stone said. “Yet we give 16-year-olds the chance to take a road test with no idea what’s going on in their brains.”
This brings us back to the fact that, in building tech modeled after the human brain—which has evolved over millions of years—we’re working towards an end whose means we don’t fully comprehend, be it something as basic as choosing when to brake or accelerate or something as complex as measuring consciousness.
“We shouldn’t charge ahead and do things just because we can,” Stone said. “The technology can be very powerful, which is exciting, but we have to consider its implications.”
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