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#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

#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

#435712 U.S. Energy Department is First Customer ...

Argonne National Laboratory and Lawrence Livermore National Laboratory will be among the first organizations to install AI computers made from the largest silicon chip ever built. Last month, Cerebras Systems unveiled a 46,225-square millimeter chip with 1.2 trillion transistors designed to speed the training of neural networks. Today, such training is often done in large data centers using GPU-based servers. Cerebras plans to begin selling computers based on the notebook-size chip in the 4th quarter of this year.

“The opportunity to incorporate the largest and fastest AI chip ever—the Cerebras WSE—into our advanced computing infrastructure will enable us to dramatically accelerate our deep learning research in science, engineering, and health” Rick Stevens, head of computing at Argonne National Laboratory, said in a press release. “It will allow us to invent and test more algorithms, to more rapidly explore ideas, and to more quickly identify opportunities for scientific progress.”

Argonne and Lawrence Livermore are the first DOE entities to participate in what is expected to be a multi-year, multi-lab partnership. Cerebras plans to expand to other laboratories in the coming months.

Cerebras computers will be integrated into existing supercomputers at the two DOE labs to act as AI accelerators for those machines. In 2021, Argonne plans to become home to the United States’ first exascale computer, named Aurora; it will be capable of more than 1 billion billion calculations per second. Intel and Cray are the leaders on that $500 million project. The national laboratory is already home to Mira, the 24th-most powerful supercomputer in the world, and Theta, the 28th-most powerful. Lawrence Livermore is also on track to achieve exascale with El Capitan, a $600-million, 1.5-exaflop machine set to go live in late 2022. The lab is also home to the number-two-ranked Sierra supercomputer and the number-10-ranked Lassen.

The U.S. Energy Department established the Artificial Intelligence and Technology Office earlier this month to better take advantage of AI for solving the kinds of problems the U.S. national laboratories tackle. 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

#435703 FarmWise Raises $14.5 Million to Teach ...

We humans spend most of our time getting hungry or eating, which must be really inconvenient for the people who have to produce food for everyone. For a sustainable and tasty future, we’ll need to make the most of what we’ve got by growing more food with less effort, and that’s where the robots can help us out a little bit.

FarmWise, a California-based startup, is looking to enhance farming efficiency by automating everything from seeding to harvesting, starting with the worst task of all: weeding. And they’ve just raised US $14.5 million to do it.

FarmWise’s autonomous, AI-enabled robots are designed to solve farmers’ most pressing challenges by performing a variety of farming functions – starting with weeding, and providing personalized care to every plant they touch. Using machine learning models, computer vision and high-precision mechanical tools, FarmWise’s sophisticated robots cleanly pick weeds from fields, leaving crops with the best opportunity to thrive while eliminating harmful chemical inputs. To date, FarmWise’s robots have efficiently removed weeds from more than 10 million plants.

FarmWise is not the first company to work on large mobile farming robots. A few years ago, we wrote about DeepField Robotics and their giant weed-punching robot. But considering how many humans there are, and how often we tend to get hungry, it certainly seems like there’s plenty of opportunity to go around.

Photo: FarmWise

FarmWise is collecting massive amounts of data about every single plant in an entire field, which is something that hasn’t been possible before. Above, one of the robots at a farm in Salinas Valley, Calif.

Weeding is just one thing that farm robots are able to do. FarmWise is collecting massive amounts of data about every single plant in an entire field, practically on the per-leaf level, which is something that hasn’t been possible before. Data like this could be used for all sorts of things, but generally, the long-term hope is that robots could tend to every single plant individually—weeding them, fertilizing them, telling them what good plants they are, and then mercilessly yanking them out of the ground at absolute peak ripeness. It’s not realistic to do this with human labor, but it’s the sort of data-intensive and monotonous task that robots could be ideal for.

The question with robots like this is not necessarily whether they can do the job that they were created for, because generally, they can—farms are structured enough environments that they lend themselves to autonomous robots, and the tasks are relatively well defined. The issue right now, I think, is whether robots are really time- and cost-effective for farmers. Capable robots are an expensive investment, and even if there is a shortage of human labor, will robots perform well enough to convince farmers to adopt the technology? That’s a solid maybe, and here’s hoping that FarmWise can figure out how to make it work.

[ FarmWise ] Continue reading

Posted in Human Robots