Tag Archives: real

#435733 Robot Squid and Robot Scallop Showcase ...

Most underwater robots use one of two ways of getting around. Way one is with propellers, and way two is with fins. But animals have shown us that there are many more kinds of underwater locomotion, potentially offering unique benefits to robots. We’ll take a look at two papers from ICRA this year that showed bioinspired underwater robots moving in creative new ways: A jet-powered squid robot that can leap out of the water, plus a robotic scallop that moves just like the real thing.

Image: Beihang University

Prototype of the squid robot in (a) open and (b) folded states. The soft fins and arms are controlled by pneumatic actuators.

This “squid-like aquatic-aerial vehicle” from Beihang University in China is modeled after flying squids. Real squids, in addition to being tasty, propel themselves using water jets, and these jets are powerful enough that some squids can not only jump out of the water, but actually achieve controlled flight for a brief period by continuing to jet while in the air. The flight phase is extended through the use of fins as arms and wings to generate a little bit of lift. Real squids use this multimodal propulsion to escape predators, and it’s also much faster—a squid can double its normal swimming speed while in the air, flying at up to 50 body lengths per second.

The squid robot is powered primarily by compressed air, which it stores in a cylinder in its nose (do squids have noses?). The fins and arms are controlled by pneumatic actuators. When the robot wants to move through the water, it opens a value to release a modest amount of compressed air; releasing the air all at once generates enough thrust to fire the robot squid completely out of the water.

The jumping that you see at the end of the video is preliminary work; we’re told that the robot squid can travel between 10 and 20 meters by jumping, whereas using its jet underwater will take it just 10 meters. At the moment, the squid can only fire its jet once, but the researchers plan to replace the compressed air with something a bit denser, like liquid CO2, which will allow for extended operation and multiple jumps. There’s also plenty of work to do with using the fins for dynamic control, which the researchers say will “reveal the superiority of the natural flying squid movement.”

“Design and Experiments of a Squid-like Aquatic-aerial Vehicle With Soft Morphing Fins and Arms,” by Taogang Hou, Xingbang Yang, Haohong Su, Buhui Jiang, Lingkun Chen, Tianmiao Wang, and Jianhong Liang from Beihang University in China, was presented at ICRA 2019 in Montreal.

Image: EPFL

The EPFL researchers studied the morphology and function of a real scallop (a) to design their robot scallop (b), which consists of two shells connected at a hinge and enclosed by a flexible elastic membrane. The robot and animal both swim by rapidly, cyclicly opening and closing their shells to generate water jets for propulsion. When the robot shells open, water is drawn into the body through rear openings near the hinge. When the shells close rapidly, the water is forced out, propelling the robot forward (c).

RoboScallop, a “bivalve inspired swimming robot,” comes from EPFL’s Reconfigurable Robotics Laboratory, headed by Jamie Paik. Real scallops, in addition to being tasty, propel themselves by opening and closing their shells to generate jets of water out of their backsides. By repetitively opening their shells slowly and then closing quickly, scallops can generate forward thrust in a way that’s completely internal to their bodies. Relative to things like fins or spinning propellers, a scallop is simple and robust, especially as you scale down or start looking at large swarms of robots. The EPFL researchers describe their robotic scallop as representing “a unique combination of robust to hazards or sustained use, safe in delicate environments, and simple by design.”

And here’s how the real thing looks:

As you can see from the video, RoboScallop is safe to handle even while it’s operating, although a gentle nibbling is possible if you get too handsy with it. Since the robot sucks water in and then jets it out immediately, the design is resistant to fouling, which can be a significant problem in marine environments. The RoboScallop prototype weighs 65 grams, and tops out at a brisk 16 centimeters per second, while clapping (that’s the actual technical) at just over 2.5 Hz. While RoboScallop doesn’t yet steer, real scallops can change direction by jetting out more water on one side than the other, and RoboScallop should be able to do this as well. The researchers also suggest that RoboScallop itself could even double as a gripper, which as far as I know, is not something that real scallops can do.

“RoboScallop: A Bivalve-Inspired Swimming Robot,” by Matthew A. Robertson, Filip Efremov, and Jamie Paik, was presented at ICRA 2019 in Montreal. Continue reading

Posted in Human Robots

#435731 Video Friday: NASA Is Sending This ...

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!):

MARSS 2019 – July 1-5, 2019 – Helsinki, Finland
ICRES 2019 – July 29-30, 2019 – London, UK
DARPA SubT Tunnel Circuit – August 15-22, 2019 – Pittsburgh, PA, USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

The big news today is that NASA is sending a robot to Saturn’s moon Titan. A flying robot. The Dragonfly mission will launch in 2026 and arrive in 2034, but you knew that already, because last January, we posted a detailed article about the concept from the Applied Physics Lab at Johns Hopkins University. And now it’s not a concept anymore, yay!

Again, read all the details plus an interview in 2018 article.

[ NASA ]

A robotic gripping arm that uses engineered bacteria to “taste” for a specific chemical has been developed by engineers at the University of California, Davis, and Carnegie Mellon University. The gripper is a proof-of-concept for biologically-based soft robotics.

The new device uses a biosensing module based on E. coli bacteria engineered to respond to the chemical IPTG by producing a fluorescent protein. The bacterial cells reside in wells with a flexible, porous membrane that allows chemicals to enter but keeps the cells inside. This biosensing module is built into the surface of a flexible gripper on a robotic arm, so the gripper can “taste” the environment through its fingers.

When IPTG crosses the membrane into the chamber, the cells fluoresce and electronic circuits inside the module detect the light. The electrical signal travels to the gripper’s control unit, which can decide whether to pick something up or release it.

[ UC Davis ]

The Toyota Research Institute (TRI) is taking on the hard problems in manipulation research toward making human-assist robots reliable and robust. Dr. Russ Tedrake, TRI Vice President of Robotics Research, explains how we are exploring the challenges and addressing the reliability gap by using a robot loading dishes in a dishwasher as an example task.

[ TRI ]

The Tactile Telerobot is the world’s first haptic telerobotic system that transmits realistic touch feedback to an operator located anywhere in the world. It is the product of joint collaboration between Shadow Robot Company, HaptX, and SynTouch. All Nippon Airways funded the project’s initial research and development.

What’s really unique about this is the HaptX tactile feedback system, which is something we’ve been following for several years now. It’s one of the most magical tech experiences I’ve ever had, and you can read about it here and here.

[ HaptX ]

Thanks Andrew!

I love how snake robots can emulate some of the fanciest moves of real snakes, and then also do bonkers things that real snakes never do.

[ Matsuno Lab ]

Here are a couple interesting videos from the Human-Robot Interaction Lab at Tufts.

A robot is instructed to perform an action and cannot do it due to lack of sensors. But when another robot is placed nearby, it can execute the instruction by tacitly tapping into the other robot’s mind and using that robot’s sensors for its own actions. Yes, it’s automatic, and yes, it’s the BORG!

Two Nao robots are instructed to perform a dance and are able to do it right after instruction. Moreover, they can switch roles immediately, and even a third different PR2 robot can perform the dance right away, demonstrating the ability of our DIARC architecture to learn quickly and share the knowledge with any type of robot running the architecture.

Compared to Nao, PR2 just sounds… depressed.

[ HRI Lab ]

This work explores the problem of robot tool construction – creating tools from parts available in the environment. We advance the state-of-the-art in robotic tool construction by introducing an approach that enables the robot to construct a wider range of tools with greater computational efficiency. Specifically, given an action that the robot wishes to accomplish and a set of building parts available to the robot, our approach reasons about the shape of the parts and potential ways of attaching them, generating a ranking of part combinations that the robot then uses to construct and test the target tool. We validate our approach on the construction of five tools using a physical 7-DOF robot arm.

[ RAIL Lab ] via [ RSS ]

We like Magazino’s approach to warehouse picking- constrain the problem to something you can reliably solve, like shoeboxes.

Magazino has announced a new pricing model for their robots. You pay 55k euros for the robot itself, and then after that, all you pay to keep the robot working is 6 cents per pick, so the robot is only costing you money for the work that it actually does.

[ Magazino ]

Thanks Florin!

Human-Robot Collaborations are happening across factories worldwide, yet very few are using it for smaller businesses, due to high costs or the difficulty of customization. Elephant Robotics, a new player from Shenzhen, the Silicon Valley of Asia, has set its sight on helping smaller businesses gain access to smart robotics. They created a Catbot (a collaborative robotic arm) that will offer high efficiency and flexibility to various industries.

The Catbot is set to help from education projects, photography, massaging, to being a personal barista or co-playing a table game. The customizations are endless. To increase the flexibility of usage, the Catbot is extremely easy to program from a high precision task up to covering hefty ground projects.

[ Elephant Robotics ]

Thanks Johnson!

Dronistics, an EPFL spin-off, has been testing out their enclosed delivery drone in the Dominican Republic through a partnership with WeRobotics.

[ WeRobotics ]

QTrobot is an expressive humanoid robot designed to help children with autism spectrum disorder and children with special educational needs in learning new skills. QTrobot uses simple and exaggerated facial expressions combined by interactive games and stories, to help children improve their emotional skills. QTrobot helps children to learn about and better understand the emotions and teach them strategies to handle their emotions more effectively.

[ LuxAI ]

Here’s a typical day in the life of a Tertill solar-powered autonomous weed-destroying robot.

$300, now shipping from Franklin Robotics.

[ Tertill ]

PAL Robotics is excited to announce a new TIAGo with two arms, TIAGo++! After carefully listening to the robotics community needs, we used TIAGo’s modularity to integrate two 7-DoF arms to our mobile manipulator. TIAGo++ can help you swiftly accomplish your research goals, opening endless possibilities in mobile manipulation.

[ PAL Robotics ]

Thanks Jack!

You’ve definitely already met the Cobalt security robot, but Toyota AI Ventures just threw a pile of money at them and would therefore like you to experience this re-introduction:

[ Cobalt Robotics ] via [ Toyota AI ]

ROSIE is a mobile manipulator kit from HEBI Robotics. And if you don’t like ROSIE, the modular nature of HEBI’s hardware means that you can take her apart and make something more interesting.

[ HEBI Robotics ]

Learn about Kawasaki Robotics’ second addition to their line of duAro dual-arm collaborative robots, duAro2. This model offers an extended vertical reach (550 mm) and an increased payload capacity (3 kg/arm).

[ Kawasaki Robotics ]

Drone Delivery Canada has partnered with Peel Region Paramedics to pilot its proprietary drone delivery platform to enable rapid first responder technology via drone with the goal to reduce response time and potentially save lives.

[ Drone Delivery Canada ]

In this week’s episode of Robots in Depth, Per speaks with Harri Ketamo, from Headai.

Harri Ketamo talks about AI and how he aims to mimic human decision making with algorithms. Harri has done a lot of AI for computer games to create opponents that are entertaining to play against. It is easy to develop a very bad or a very good opponent, but designing an opponent that behaves like a human, is entertaining to play against and that you can beat is quite hard. He talks about how AI in computer games is a very important story telling tool and an important part of making a game entertaining to play.

This work led him into other parts of the AI field. Harri thinks that we sometimes have a problem separating what is real from what is the type of story telling he knows from gaming AI. He calls for critical analysis of AI and says that data has to be used to verify AI decisions and results.

[ Robots in Depth ]

Thanks Per! Continue reading

Posted in Human Robots

#435707 AI Agents Startle Researchers With ...

After 25 million games, the AI agents playing hide-and-seek with each other had mastered four basic game strategies. The researchers expected that part.

After a total of 380 million games, the AI players developed strategies that the researchers didn’t know were possible in the game environment—which the researchers had themselves created. That was the part that surprised the team at OpenAI, a research company based in San Francisco.

The AI players learned everything via a machine learning technique known as reinforcement learning. In this learning method, AI agents start out by taking random actions. Sometimes those random actions produce desired results, which earn them rewards. Via trial-and-error on a massive scale, they can learn sophisticated strategies.

In the context of games, this process can be abetted by having the AI play against another version of itself, ensuring that the opponents will be evenly matched. It also locks the AI into a process of one-upmanship, where any new strategy that emerges forces the opponent to search for a countermeasure. Over time, this “self-play” amounted to what the researchers call an “auto-curriculum.”

According to OpenAI researcher Igor Mordatch, this experiment shows that self-play “is enough for the agents to learn surprising behaviors on their own—it’s like children playing with each other.”

Reinforcement is a hot field of AI research right now. OpenAI’s researchers used the technique when they trained a team of bots to play the video game Dota 2, which squashed a world-champion human team last April. The Alphabet subsidiary DeepMind has used it to triumph in the ancient board game Go and the video game StarCraft.

Aniruddha Kembhavi, a researcher at the Allen Institute for Artificial Intelligence (AI2) in Seattle, says games such as hide-and-seek offer a good way for AI agents to learn “foundational skills.” He worked on a team that taught their AllenAI to play Pictionary with humans, viewing the gameplay as a way for the AI to work on common sense reasoning and communication. “We are, however, quite far away from being able to translate these preliminary findings in highly simplified environments into the real world,” says Kembhavi.

Illustration: OpenAI

AI agents construct a fort during a hide-and-seek game developed by OpenAI.

In OpenAI’s game of hide-and-seek, both the hiders and the seekers received a reward only if they won the game, leaving the AI players to develop their own strategies. Within a simple 3D environment containing walls, blocks, and ramps, the players first learned to run around and chase each other (strategy 1). The hiders next learned to move the blocks around to build forts (2), and then the seekers learned to move the ramps (3), enabling them to jump inside the forts. Then the hiders learned to move all the ramps into their forts before the seekers could use them (4).

The two strategies that surprised the researchers came next. First the seekers learned that they could jump onto a box and “surf” it over to a fort (5), allowing them to jump in—a maneuver that the researchers hadn’t realized was physically possible in the game environment. So as a final countermeasure, the hiders learned to lock all the boxes into place (6) so they weren’t available for use as surfboards.

Illustration: OpenAI

An AI agent uses a nearby box to surf its way into a competitor’s fort.

In this circumstance, having AI agents behave in an unexpected way wasn’t a problem: They found different paths to their rewards, but didn’t cause any trouble. However, you can imagine situations in which the outcome would be rather serious. Robots acting in the real world could do real damage. And then there’s Nick Bostrom’s famous example of a paper clip factory run by an AI, whose goal is to make as many paper clips as possible. As Bostrom told IEEE Spectrum back in 2014, the AI might realize that “human bodies consist of atoms, and those atoms could be used to make some very nice paper clips.”

Bowen Baker, another member of the OpenAI research team, notes that it’s hard to predict all the ways an AI agent will act inside an environment—even a simple one. “Building these environments is hard,” he says. “The agents will come up with these unexpected behaviors, which will be a safety problem down the road when you put them in more complex environments.”

AI researcher Katja Hofmann at Microsoft Research Cambridge, in England, has seen a lot of gameplay by AI agents: She started a competition that uses Minecraft as the playing field. She says the emergent behavior seen in this game, and in prior experiments by other researchers, shows that games can be a useful for studies of safe and responsible AI.

“I find demonstrations like this, in games and game-like settings, a great way to explore the capabilities and limitations of existing approaches in a safe environment,” says Hofmann. “Results like these will help us develop a better understanding on how to validate and debug reinforcement learning systems–a crucial step on the path towards real-world applications.”

Baker says there’s also a hopeful takeaway from the surprises in the hide-and-seek experiment. “If you put these agents into a rich enough environment they will find strategies that we never knew were possible,” he says. “Maybe they can solve problems that we can’t imagine solutions to.” Continue reading

Posted in Human Robots

#435681 Video Friday: This NASA Robot Uses ...

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
Let us know if you have suggestions for next week, and enjoy today’s videos.

Robots can land on the Moon and drive on Mars, but what about the places they can’t reach? Designed by engineers as NASA’s Jet Propulsion Laboratory in Pasadena, California, a four-limbed robot named LEMUR (Limbed Excursion Mechanical Utility Robot) can scale rock walls, gripping with hundreds of tiny fishhooks in each of its 16 fingers and using artificial intelligence to find its way around obstacles. In its last field test in Death Valley, California, in early 2019, LEMUR chose a route up a cliff, scanning the rock for ancient fossils from the sea that once filled the area.

The LEMUR project has since concluded, but it helped lead to a new generation of walking, climbing and crawling robots. In future missions to Mars or icy moons, robots with AI and climbing technology derived from LEMUR could discover similar signs of life. Those robots are being developed now, honing technology that may one day be part of future missions to distant worlds.

[ NASA ]

This video demonstrates the autonomous footstep planning developed by IHMC. Robots in this video are the Atlas humanoid robot (DRC version) and the NASA Valkyrie. The operator specifies a goal location in the world, which is modeled as planar regions using the robot’s perception sensors. The planner then automatically computes the necessary steps to reach the goal using a Weighted A* algorithm. The algorithm does not reject footholds that have a certain amount of support, but instead modifies them after the plan is found to try and increase that support area.

Currently, narrow terrain has a success rate of about 50%, rough terrain is about 90%, whereas flat ground is near 100%. We plan on increasing planner speed and the ability to plan through mazes and to unseen goals by including a body-path planner as the first step. Control, Perception, and Planning algorithms by IHMC Robotics.

[ IHMC ]

I’ve never really been able to get into watching people play poker, but throw an AI from CMU and Facebook into a game of no-limit Texas hold’em with five humans, and I’m there.

[ Facebook ]

In this video, Cassie Blue is navigating autonomously. Right now, her world is very small, the Wavefield at the University of Michigan, where she is told to turn left at intersections. You’re right, that is not a lot of independence, but it’s a first step away from a human and an RC controller!

Using a RealSense RGBD Camera, an IMU, and our version of an InEKF with contact factors, Cassie Blue is building a 3D semantic map in real time that identifies sidewalks, grass, poles, bicycles, and buildings. From the semantic map, occupancy and cost maps are built with the sidewalk identified as walk-able area and everything else considered as an obstacle. A planner then sets a goal to stay approximately 50 cm to the right of the sidewalk’s left edge and plans a path around obstacles and corners using D*. The path is translated into way-points that are achieved via Cassie Blue’s gait controller.

[ University of Michigan ]

Thanks Jesse!

Dave from HEBI Robotics wrote in to share some new actuators that are designed to get all kinds of dirty: “The R-Series takes HEBI’s X-Series to the next level, providing a sealed robotics solution for rugged, industrial applications and laying the groundwork for industrial users to address challenges that are not well met by traditional robotics. To prove it, we shot some video right in the Allegheny River here in Pittsburgh. Not a bad way to spend an afternoon :-)”

The R-Series Actuator is a full-featured robotic component as opposed to a simple servo motor. The output rotates continuously, requires no calibration or homing on boot-up, and contains a thru-bore for easy daisy-chaining of wiring. Modular in nature, R-Series Actuators can be used in everything from wheeled robots to collaborative robotic arms. They are sealed to IP67 and designed with a lightweight form factor for challenging field applications, and they’re packed with sensors that enable simultaneous control of position, velocity, and torque.

[ HEBI Robotics ]

Thanks Dave!

If your robot hands out karate chops on purpose, that’s great. If it hands out karate chops accidentally, maybe you should fix that.

COVR is short for “being safe around collaborative and versatile robots in shared spaces”. Our mission is to significantly reduce the complexity in safety certifying cobots. Increasing safety for collaborative robots enables new innovative applications, thus increasing production and job creation for companies utilizing the technology. Whether you’re an established company seeking to deploy cobots or an innovative startup with a prototype of a cobot related product, COVR will help you analyze, test and validate the safety for that application.

[ COVR ]

Thanks Anna!

EPFL startup Flybotix has developed a novel drone with just two propellers and an advanced stabilization system that allow it to fly for twice as long as conventional models. That fact, together with its small size, makes it perfect for inspecting hard-to-reach parts of industrial facilities such as ducts.

[ Flybotix ]

SpaceBok is a quadruped robot designed and built by a Swiss student team from ETH Zurich and ZHAW Zurich, currently being tested using Automation and Robotics Laboratories (ARL) facilities at our technical centre in the Netherlands. The robot is being used to investigate the potential of ‘dynamic walking’ and jumping to get around in low gravity environments.

SpaceBok could potentially go up to 2 m high in lunar gravity, although such a height poses new challenges. Once it comes off the ground the legged robot needs to stabilise itself to come down again safely – like a mini-spacecraft. So, like a spacecraft. SpaceBok uses a reaction wheel to control its orientation.

[ ESA ]

A new video from GITAI showing progress on their immersive telepresence robot for space.

[ GITAI ]

Tech United’s HERO robot (a Toyota HSR) competed in the RoboCup@Home competition, and it had a couple of garbage-related hiccups.

[ Tech United ]

Even small drones are getting better at autonomous obstacle avoidance in cluttered environments at useful speeds, as this work from the HKUST Aerial Robotics Group shows.

[ HKUST ]

DelFly Nimbles now come in swarms.

[ DelFly Nimble ]

This is a very short video, but it’s a fairly impressive look at a Baxter robot collaboratively helping someone put a shirt on, a useful task for folks with disabilities.

[ Shibata Lab ]

ANYmal can inspect the concrete in sewers for deterioration by sliding its feet along the ground.

[ ETH Zurich ]

HUG is a haptic user interface for teleoperating advanced robotic systems as the humanoid robot Justin or the assistive robotic system EDAN. With its lightweight robot arms, HUG can measure human movements and simultaneously display forces from the distant environment. In addition to such teleoperation applications, HUG serves as a research platform for virtual assembly simulations, rehabilitation, and training.

[ DLR ]

This video about “image understanding” from CMU in 1979 (!) is amazing, and even though it’s long, you won’t regret watching until 3:30. Or maybe you will.

[ ARGOS (pdf) ]

Will Burrard-Lucas’ BeetleCam turned 10 this month, and in this video, he recounts the history of his little robotic camera.

[ BeetleCam ]

In this week’s episode of Robots in Depth, Per speaks with Gabriel Skantze from Furhat Robotics.

Gabriel Skantze is co-founder and Chief Scientist at Furhat Robotics and Professor in speech technology at KTH with a specialization in conversational systems. He has a background in research into how humans use spoken communication to interact.

In this interview, Gabriel talks about how the social robot revolution makes it necessary to communicate with humans in a human ways through speech and facial expressions. This is necessary as we expand the number of people that interact with robots as well as the types of interaction. Gabriel gives us more insight into the many challenges of implementing spoken communication for co-bots, where robots and humans work closely together. They need to communicate about the world, the objects in it and how to handle them. We also get to hear how having an embodied system using the Furhat robot head helps the interaction between humans and the system.

[ Robots in Depth ] Continue reading

Posted in Human Robots

#435676 Intel’s Neuromorphic System Hits 8 ...

At the DARPA Electronics Resurgence Initiative Summit today in Detroit, Intel plans to unveil an 8-million-neuron neuromorphic system comprising 64 Loihi research chips—codenamed Pohoiki Beach. Loihi chips are built with an architecture that more closely matches the way the brain works than do chips designed to do deep learning or other forms of AI. For the set of problems that such “spiking neural networks” are particularly good at, Loihi is about 1,000 times as fast as a CPU and 10,000 times as energy efficient. The new 64-Loihi system represents the equivalent of 8-million neurons, but that’s just a step to a 768-chip, 100-million-neuron system that the company plans for the end of 2019.

Intel and its research partners are just beginning to test what massive neural systems like Pohoiki Beach can do, but so far the evidence points to even greater performance and efficiency, says Mike Davies, director of neuromorphic research at Intel.

“We’re quickly accumulating results and data that there are definite benefits… mostly in the domain of efficiency. Virtually every one that we benchmark…we find significant gains in this architecture,” he says.

Going from a single-Loihi to 64 of them is more of a software issue than a hardware one. “We designed scalability into the Loihi chip from the beginning,” says Davies. “The chip has a hierarchical routing interface…which allows us to scale to up to 16,000 chips. So 64 is just the next step.”

Photo: Tim Herman/Intel Corporation

One of Intel’s Nahuku boards, each of which contains 8 to 32 Intel Loihi neuromorphic chips, shown here interfaced to an Intel Arria 10 FPGA development kit. Intel’s latest neuromorphic system, Pohoiki Beach, is made up of multiple Nahuku boards and contains 64 Loihi chips.

Finding algorithms that run well on an 8-million-neuron system and optimizing those algorithms in software is a considerable effort, he says. Still, the payoff could be huge. Neural networks that are more brain-like, such as Loihi, could be immune to some of the artificial intelligence’s—for lack of a better word—dumbness.

For example, today’s neural networks suffer from something called catastrophic forgetting. If you tried to teach a trained neural network to recognize something new—a new road sign, say—by simply exposing the network to the new input, it would disrupt the network so badly that it would become terrible at recognizing anything. To avoid this, you have to completely retrain the network from the ground up. (DARPA’s Lifelong Learning, or L2M, program is dedicated to solving this problem.)

(Here’s my favorite analogy: Say you coached a basketball team, and you raised the net by 30 centimeters while nobody was looking. The players would miss a bunch at first, but they’d figure things out quickly. If those players were like today’s neural networks, you’d have to pull them off the court and teach them the entire game over again—dribbling, passing, everything.)

Loihi can run networks that might be immune to catastrophic forgetting, meaning it learns a bit more like a human. In fact, there’s evidence through a research collaboration with Thomas Cleland’s group at Cornell University, that Loihi can achieve what’s called one-shot learning. That is, learning a new feature after being exposed to it only once. The Cornell group showed this by abstracting a model of the olfactory system so that it would run on Loihi. When exposed to a new virtual scent, the system not only didn't catastrophically forget everything else it had smelled, it learned to recognize the new scent just from the single exposure.

Loihi might also be able to run feature-extraction algorithms that are immune to the kinds of adversarial attacks that befuddle today’s image recognition systems. Traditional neural networks don’t really understand the features they’re extracting from an image in the way our brains do. “They can be fooled with simplistic attacks like changing individual pixels or adding a screen of noise that wouldn’t fool a human in any way,” Davies explains. But the sparse-coding algorithms Loihi can run work more like the human visual system and so wouldn’t fall for such shenanigans. (Disturbingly, humans are not completely immune to such attacks.)

Photo: Tim Herman/Intel Corporation

A close-up shot of Loihi, Intel’s neuromorphic research chip. Intel’s latest neuromorphic system, Pohoiki Beach, will be comprised of 64 of these Loihi chips.

Researchers have also been using Loihi to improve real-time control for robotic systems. For example, last week at the Telluride Neuromorphic Cognition Engineering Workshop—an event Davies called “summer camp for neuromorphics nerds”—researchers were hard at work using a Loihi-based system to control a foosball table. “It strikes people as crazy,” he says. “But it’s a nice illustration of neuromorphic technology. It’s fast, requires quick response, quick planning, and anticipation. These are what neuromorphic chips are good at.” Continue reading

Posted in Human Robots