Tag Archives: vision

#435806 Boston Dynamics’ Spot Robot Dog ...

Posted on September 24, 2019 by Android

Boston Dynamics is announcing this morning that Spot, its versatile quadruped robot, is now for sale. The machine’s animal-like behavior regularly electrifies crowds at tech conferences, and like other Boston Dynamics’ robots, Spot is a YouTube sensation whose videos amass millions of views.

Now anyone interested in buying a Spot—or a pack of them—can go to the company’s website and submit an order form. But don’t pull out your credit card just yet. Spot may cost as much as a luxury car, and it is not really available to consumers. The initial sale, described as an “early adopter program,” is targeting businesses. Boston Dynamics wants to find customers in select industries and help them deploy Spots in real-world scenarios.

“What we’re doing is the productization of Spot,” Boston Dynamics CEO Marc Raibert tells IEEE Spectrum. “It’s really a milestone for us going from robots that work in the lab to these that are hardened for work out in the field.”

Boston Dynamics has always been a secretive company, but last month, in preparation for launching Spot (formerly SpotMini), it allowed our photographers into its headquarters in Waltham, Mass., for a special shoot. In that session, we captured Spot and also Atlas—the company’s highly dynamic humanoid—in action, walking, climbing, and jumping.

You can see Spot’s photo interactives on our Robots Guide. (The Atlas interactives will appear in coming weeks.)

Gif: Bob O’Connor/Robots.ieee.org

And if you’re in the market for a robot dog, here’s everything we know about Boston Dynamics’ plans for Spot.

Who can buy a Spot?
If you’re interested in one, you should go to Boston Dynamics’ website and take a look at the information the company requires from potential buyers. Again, the focus is on businesses. Boston Dynamics says it wants to get Spots out to initial customers that “either have a compelling use case or a development team that we believe can do something really interesting with the robot,” says VP of business development Michael Perry. “Just because of the scarcity of the robots that we have, we’re going to have to be selective about which partners we start working together with.”

What can Spot do?
As you’ve probably seen on the YouTube videos, Spot can walk, trot, avoid obstacles, climb stairs, and much more. The robot’s hardware is almost completely custom, with powerful compute boards for control, and five sensor modules located on every side of Spot’s body, allowing it to survey the space around itself from any direction. The legs are powered by 12 custom motors with a reduction, with a top speed of 1.6 meters per second. The robot can operate for 90 minutes on a charge. In addition to the basic configuration, you can integrate up to 14 kilograms of extra hardware to a payload interface. Among the payload packages Boston Dynamics plans to offer are a 6 degrees-of-freedom arm, a version of which can be seen in some of the YouTube videos, and a ring of cameras called SpotCam that could be used to create Street View–type images inside buildings.

Image: Boston Dynamics

How do you control Spot?
Learning to drive the robot using its gaming-style controller “takes 15 seconds,” says CEO Marc Raibert. He explains that while teleoperating Spot, you may not realize that the robot is doing a lot of the work. “You don’t really see what that is like until you’re operating the joystick and you go over a box and you don’t have to do anything,” he says. “You’re practically just thinking about what you want to do and the robot takes care of everything.” The control methods have evolved significantly since the company’s first quadruped robots, machines like BigDog and LS3. “The control in those days was much more monolithic, and now we have what we call a sequential composition controller,” Raibert says, “which lets the system have control of the dynamics in a much broader variety of situations.” That means that every time one of Spot’s feet touches or doesn’t touch the ground, this different state of the body affects the basic physical behavior of the robot, and the controller adjusts accordingly. “Our controller is designed to understand what that state is and have different controls depending upon the case,” he says.

How much does Spot cost?
Boston Dynamics would not give us specific details about pricing, saying only that potential customers should contact them for a quote and that there is going to be a leasing option. It’s understandable: As with any expensive and complex product, prices can vary on a case by case basis and depend on factors such as configuration, availability, level of support, and so forth. When we pressed the company for at least an approximate base price, Perry answered: “Our general guidance is that the total cost of the early adopter program lease will be less than the price of a car—but how nice a car will depend on the number of Spots leased and how long the customer will be leasing the robot.”

Can Spot do mapping and SLAM out of the box?
The robot’s perception system includes cameras and 3D sensors (there is no lidar), used to avoid obstacles and sense the terrain so it can climb stairs and walk over rubble. It’s also used to create 3D maps. According to Boston Dynamics, the first software release will offer just teleoperation. But a second release, to be available in the next few weeks, will enable more autonomous behaviors. For example, it will be able to do mapping and autonomous navigation—similar to what the company demonstrated in a video last year, showing how you can drive the robot through an environment, create a 3D point cloud of the environment, and then set waypoints within that map for Spot to go out and execute that mission. For customers that have their own autonomy stack and are interested in using those on Spot, Boston Dynamics made it “as plug and play as possible in terms of how third-party software integrates into Spot’s system,” Perry says. This is done mainly via an API.

How does Spot’s API works?
Boston Dynamics built an API so that customers can create application-level products with Spot without having to deal with low-level control processes. “Rather than going and building joint-level kinematic access to the robot,” Perry explains, “we created a high-level API and SDK that allows people who are used to Web app development or development of missions for drones to use that same scope, and they’ll be able to build applications for Spot.”

What applications should we see first?
Boston Dynamics envisions Spot as a platform: a versatile mobile robot that companies can use to build applications based on their needs. What types of applications? The company says the best way to find out is to put Spot in the hands of as many users as possible and let them develop the applications. Some possibilities include performing remote data collection and light manipulation in construction sites; monitoring sensors and infrastructure at oil and gas sites; and carrying out dangerous missions such as bomb disposal and hazmat inspections. There are also other promising areas such as security, package delivery, and even entertainment. “We have some initial guesses about which markets could benefit most from this technology, and we’ve been engaging with customers doing proof-of-concept trials,” Perry says. “But at the end of the day, that value story is really going to be determined by people going out and exploring and pushing the limits of the robot.”

Photo: Bob O'Connor

How many Spots have been produced?
Last June, Boston Dynamics said it was planning to build about a hundred Spots by the end of the year, eventually ramping up production to a thousand units per year by the middle of this year. The company admits that it is not quite there yet. It has built close to a hundred beta units, which it has used to test and refine the final design. This version is now being mass manufactured, but the company is still “in the early tens of robots,” Perry says.

How did Boston Dynamics test Spot?

The company has tested the robots during proof-of-concept trials with customers, and at least one is already using Spot to survey construction sites. The company has also done reliability tests at its facility in Waltham, Mass. “We drive around, not quite day and night, but hundreds of miles a week, so that we can collect reliability data and find bugs,” Raibert says.

What about competitors?
In recent years, there’s been a proliferation of quadruped robots that will compete in the same space as Spot. The most prominent of these is ANYmal, from ANYbotics, a Swiss company that spun out of ETH Zurich. Other quadrupeds include Vision from Ghost Robotics, used by one of the teams in the DARPA Subterranean Challenge; and Laikago and Aliengo from Unitree Robotics, a Chinese startup. Raibert views the competition as a positive thing. “We’re excited to see all these companies out there helping validate the space,” he says. “I think we’re more in competition with finding the right need [that robots can satisfy] than we are with the other people building the robots at this point.”

Why is Boston Dynamics selling Spot now?
Boston Dynamics has long been an R&D-centric firm, with most of its early funding coming from military programs, but it says commercializing robots has always been a goal. Productizing its machines probably accelerated when the company was acquired by Google’s parent company, Alphabet, which had an ambitious (and now apparently very dead) robotics program. The commercial focus likely continued after Alphabet sold Boston Dynamics to SoftBank, whose famed CEO, Masayoshi Son, is known for his love of robots—and profits.

Which should I buy, Spot or Aibo?
Don’t laugh. We’ve gotten emails from individuals interested in purchasing a Spot for personal use after seeing our stories on the robot. Alas, Spot is not a bigger, fancier Aibo pet robot. It’s an expensive, industrial-grade machine that requires development and maintenance. If you’re maybe Jeff Bezos you could probably convince Boston Dynamics to sell you one, but otherwise the company will prioritize businesses.

What’s next for Boston Dynamics?
On the commercial side of things, other than Spot, Boston Dynamics is interested in the logistics space. Earlier this year it announced the acquisition of Kinema Systems, a startup that had developed vision sensors and deep-learning software to enable industrial robot arms to locate and move boxes. There’s also Handle, the mobile robot on whegs (wheels + legs), that can pick up and move packages. Boston Dynamics is hiring both in Waltham, Mass., and Mountain View, Calif., where Kinema was located.

Okay, can I watch a cool video now?
During our visit to Boston Dynamics’ headquarters last month, we saw Atlas and Spot performing some cool new tricks that we unfortunately are not allowed to tell you about. We hope that, although the company is putting a lot of energy and resources into its commercial programs, Boston Dynamics will still find plenty of time to improve its robots, build new ones, and of course, keep making videos. [Update: The company has just released a new Spot video, which we’ve embedded at the top of the post.][Update 2: We should have known. Boston Dynamics sure knows how to create buzz for itself: It has just released a second video, this time of Atlas doing some of those tricks we saw during our visit and couldn’t tell you about. Enjoy!]

[ Boston Dynamics ] Continue reading →

Posted in Human Robots

#435791 To Fly Solo, Racing Drones Have a Need ...

Posted on September 21, 2019 by Android

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

#435779 This Robot Ostrich Can Ride Around on ...

Posted on September 20, 2019 by Android

Proponents of legged robots say that they make sense because legs are often required to go where humans go. Proponents of wheeled robots say, “Yeah, that’s great but watch how fast and efficient my robot is, compared to yours.” Some robots try and take advantage of wheels and legs with hybrid designs like whegs or wheeled feet, but a simpler and more versatile solution is to do what humans do, and just take advantage of wheels when you need them.

We’ve seen a few experiments with this. The University of Michigan managed to convince Cassie to ride a Segway, with mostly positive (but occasionally quite negative) results. A Segway, and hoverboard-like systems, can provide wheeled mobility for legged robots over flat terrain, but they can’t handle things like stairs, which is kind of the whole point of having a robot with legs anyway.

Image: UC Berkeley

From left, a Segway, a hovercraft, and hovershoes, with complexity in terms of user control increasing from left to right.

At UC Berkeley’s Hybrid Robotics Lab, led by Koushil Sreenath, researchers have taken things a step further. They are teaching their Cassie bipedal robot (called Cassie Cal) to wheel around on a pair of hovershoes. Hovershoes are like hoverboards that have been chopped in half, resulting in a pair of motorized single-wheel skates. You balance on the skates, and control them by leaning forwards and backwards and left and right, which causes each skate to accelerate or decelerate in an attempt to keep itself upright. It’s not easy to get these things to work, even for a human, but by adding a sensor package to Cassie the UC Berkeley researchers have managed to get it to zip around campus fully autonomously.

Remember, Cassie is operating autonomously here—it’s performing vSLAM (with an Intel RealSense) and doing all of its own computation onboard in real time. Watching it jolt across that cracked sidewalk is particularly impressive, especially considering that it only has pitch control over its ankles and can’t roll its feet to maintain maximum contact with the hovershoes. But you can see the advantage that this particular platform offers to a robot like Cassie, including the ability to handle stairs. Stairs in one direction, anyway.

It’s a testament to the robustness of UC Berkeley’s controller that they were willing to let the robot operate untethered and outside, and it sounds like they’re thinking long-term about how legged robots on wheels would be real-world useful:

Our feedback control and autonomous system allow for swift movement through urban environments to aid in everything from food delivery to security and surveillance to search and rescue missions. This work can also help with transportation in large factories and warehouses.

For more details, we spoke with the UC Berkeley students (Shuxiao Chen, Jonathan Rogers, and Bike Zhang) via email.

IEEE Spectrum: How representative of Cassie’s real-world performance is what we see in the video? What happens when things go wrong?

Cassie’s real-world performance is similar to what we see in the video. Cassie can ride the hovershoes successfully all around the campus. Our current controller allows Cassie to robustly ride the hovershoes and rejects various perturbations. At present, one of the failure modes is when the hovershoe rolls to the side—this happens when it goes sideways down a step or encounters a large obstacle on one side of it, causing it to roll over. Under these circumstances, Cassie doesn’t have sufficient control authority (due to the thin narrow feet) to get the hovershoe back on its wheel.

The Hybrid Robotics Lab has been working on robots that walk over challenging terrain—how do wheeled platforms like hovershoes fit in with that?

Surprisingly, this research is related to our prior work on walking on discrete terrain. While locomotion using legs is efficient when traveling over rough and discrete terrain, wheeled locomotion is more efficient when traveling over flat continuous terrain. Enabling legged robots to ride on various micro-mobility platforms will offer multimodal locomotion capabilities, improving the efficiency of locomotion over various terrains.

Our current research furthers the locomotion ability for bipedal robots over continuous terrains by using a wheeled platform. In the long run, we would like to develop multi-modal locomotion strategies based on our current and prior work to allow legged robots to robustly and efficiently locomote in our daily life.

Photo: UC Berkeley

In their experiments, the UC Berkeley researchers say Cassie proved quite capable of riding the hovershoes over rough and uneven terrain, including going down stairs.

How long did it take to train Cassie to use the hovershoes? Are there any hovershoe skills that Cassie is better at than an average human?

We spent about eight months to develop our whole system, including a controller, a path planner, and a vision system. This involved developing mathematical models of Cassie and the hovershoes, setting up a dynamical simulation, figuring out how to interface and communicate with various sensors and Cassie, and doing several experiments to slowly improve performance. In contrast, a human with a good sense of balance needs a few hours to learn to use the hovershoes. A human who has never used skates or skis will probably need a longer time.

A human can easily turn in place on the hovershoes, while Cassie cannot do this motion currently due to our algorithm requiring a non-zero forward speed in order to turn. However, Cassie is much better at riding the hovershoes over rough and uneven terrain including riding the hovershoes down some stairs!

What would it take to make Cassie faster or more agile on the hovershoes?

While Cassie can currently move at a decent pace on the hovershoes and navigate obstacles, Cassie’s ability to avoid obstacles at rapid speeds is constrained by the sensing, the controller, and the onboard computation. To enable Cassie to dynamically weave around obstacles at high speeds exhibiting agile motions, we need to make progress on different fronts.

We need planners that take into account the entire dynamics of the Cassie-Hovershoe system and rapidly generate dynamically-feasible trajectories; we need controllers that tightly coordinate all the degrees-of-freedom of Cassie to dynamically move while balancing on the hovershoes; we need sensors that are robust to motion-blur artifacts caused due to fast turns; and we need onboard computation that can execute our algorithms at real-time speeds.

What are you working on next?

We are working on enabling more aggressive movements for Cassie on the hovershoes by fully exploiting Cassie’s dynamics. We are working on approaches that enable us to easily go beyond hovershoes to other challenging micro-mobility platforms. We are working on enabling Cassie to step onto and off from wheeled platforms such as hovershoes. We would like to create a future of multi-modal locomotion strategies for legged robots to enable them to efficiently help people in our daily life.

“Feedback Control for Autonomous Riding of Hovershoes by a Cassie Bipedal Robot,” by Shuxiao Chen, Jonathan Rogers, Bike Zhang, and Koushil Sreenath from the Hybrid Robotics Lab at UC Berkeley, has been submitted to IEEE Robotics and Automation Letters with option to be presented at the 2019 IEEE RAS International Conference on Humanoid Robots. Continue reading →

Posted in Human Robots

#435775 Jaco Is a Low-Power Robot Arm That Hooks ...

Posted on September 20, 2019 by Android

We usually think of robots as taking the place of humans in various tasks, but robots of all kinds can also enhance human capabilities. This may be especially true for people with disabilities. And while the Cybathlon competition showed what's possible when cutting-edge research robotics is paired with expert humans, that competition isn't necessarily reflective of the kind of robotics available to most people today.

Kinova Robotics's Jaco arm is an assistive robotic arm designed to be mounted on an electric wheelchair. With six degrees of freedom plus a three-fingered gripper, the lightweight carbon fiber arm is frequently used in research because it's rugged and versatile. But from the start, Kinova created it to add autonomy to the lives of people with mobility constraints.

Earlier this year, Kinova shared the story of Mary Nelson, an 11-year-old girl with spinal muscular atrophy, who uses her Jaco arm to show her horse in competition. Spinal muscular atrophy is a neuromuscular disorder that impairs voluntary muscle movement, including muscles that help with respiration, and Mary depends on a power chair for mobility.

We wanted to learn more about how Kinova designs its Jaco arm, and what that means for folks like Mary, so we spoke with both Kinova and Mary's parents to find out how much of a difference a robot arm can make.

IEEE Spectrum: How did Mary interact with the world before having her arm, and what was involved in the decision to try a robot arm in general? And why then Kinova's arm specifically?

Ryan Nelson: Mary interacts with the world much like you and I do, she just uses different tools to do so. For example, she is 100 percent independent using her computer, iPad, and phone, and she prefers to use a mouse. However, she cannot move a standard mouse, so she connects her wheelchair to each device with Bluetooth to move the mouse pointer/cursor using her wheelchair joystick.

For years, we had a Manfrotto magic arm and super clamp attached to her wheelchair and she used that much like the robotic arm. We could put a baseball bat, paint brush, toys, etc. in the super clamp so that Mary could hold the object and interact as physically able children do. Mary has always wanted to be more independent, so we knew the robotic arm was something she must try. We had seen videos of the Kinova arm on YouTube and on their website, so we reached out to them to get a trial.

Can you tell us about the Jaco arm, and how the process of designing an assistive robot arm is different from the process of designing a conventional robot arm?

Nathaniel Swenson, Director of U.S. Operations — Assistive Technologies at Kinova: Jaco is our flagship robotic arm. Inspired by our CEO's uncle and its namesake, Jacques “Jaco” Forest, it was designed as assistive technology with power wheelchair users in mind.

The primary differences between Jaco and our other robots, such as the new Gen3, which was designed to meet the needs of academic and industry research teams, are speed and power consumption. Other robots such as the Gen3 can move faster and draw slightly more power because they aren't limited by the battery size of power wheelchairs. Depending on the use case, they might not interact directly with a human being in the research setting and can safely move more quickly. Jaco is designed to move at safe speeds and make direct contact with the end user and draw very little power directly from their wheelchair.

The most important consideration in the design process of an assistive robot is the safety of the end user. Jaco users operate their robots through their existing drive controls to assist them in daily activities such as eating, drinking, and opening doors and they don't have to worry about the robot draining their chair's batteries throughout the day. The elegant design that results from meeting the needs of our power chair users has benefited subsequent iterations, [of products] such as the Gen3, as well: Kinova's robots are lightweight, extremely efficient in their power consumption, and safe for direct human-robot interaction. This is not true of conventional industrial robots.

What was the learning process like for Mary? Does she feel like she's mastered the arm, or is it a continuous learning process?

Ryan Nelson: The learning process was super quick for Mary. However, she amazes us every day with the new things that she can do with the arm. Literally within minutes of installing the arm on her chair, Mary had it figured out and was shaking hands with the Kinova rep. The control of the arm is super intuitive and the Kinova reps say that SMA (Spinal Muscular Atrophy) children are perfect users because they are so smart—they pick it up right away. Mary has learned to do many fine motor tasks with the arm, from picking up small objects like a pencil or a ruler, to adjusting her glasses on her face, to doing science experiments.

Photo: The Nelson Family

Mary uses a headset microphone to amplify her voice, and she will use the arm and finger to adjust the microphone in front of her mouth after she is done eating (also a task she mastered quickly with the arm). Additionally, Mary will use the arms to reach down and adjust her feet or leg by grabbing them with the arm and moving them to a more comfortable position. All of these examples are things she never really asked us to do, but something she needed and just did on her own, with the help of the arm.

What is the most common feedback that you get from new users of the arm? How about from experienced users who have been using the arm for a while?

Nathaniel Swenson: New users always tell us how excited they are to see what they can accomplish with their new Jaco. From day one, they are able to do things that they have longed to do without assistance from a caregiver: take a drink of water or coffee, scratch an itch, push the button to open an “accessible” door or elevator, or even feed their baby with a bottle.

The most common feedback I hear from experienced users is that Jaco has changed their life. Our experienced users like Mary are rock stars: everywhere they go, people get excited to see what they'll do next. The difference between a new user and an experienced user could be as little as two weeks. People who operate power wheelchairs every day are already expert drivers and we just add a new “gear” to their chair: robot mode. It's fun to see how quickly new users master the intuitive Jaco control modes.

What changes would you like to see in the next generation of Jaco arm?

Ryan Nelson: Titanium fingers! Make it lift heavier objects, hold heavier items like a baseball bat, machine gun, flame thrower, etc., and Mary literally said this last night: “I wish the arm moved fast enough to play the piano.”

Nathaniel Swenson: I love the idea of titanium fingers! Jaco's fingers are made from a flexible polymer and designed to avoid harm. This allows the fingers to bend or dislocate, rather than break, but it also means they are not as durable as a material like titanium. Increased payload, the ability to manipulate heavier objects, requires increased power consumption. We've struck a careful balance between providing enough strength to accomplish most medically necessary Activities of Daily Living and efficient use of the power chair's batteries.

We take Isaac Asimov's Laws of Robotics pretty seriously. When we start to combine machine guns, flame throwers, and artificial intelligence with robots, I get very nervous!

I wish the arm moved fast enough to play the piano, too! I am also a musician and I share Mary's dream of an assistive robot that would enable her to make music. In the meantime, while we work on that, please enjoy this beautiful violin piece by Manami Ito and her one-of-a-kind violin prosthesis:

To what extent could more autonomy for the arm be helpful for users? What would be involved in implementing that?

Nathaniel Swenson: Artificial intelligence, machine learning, and deep learning will introduce greater autonomy in future iterations of assistive robots. This will enable them to perform more complex tasks that aren't currently possible, and enable them to accomplish routine tasks more quickly and with less input than the current manual control requires.

For assistive robots, implementation of greater autonomy involves a focus on end-user safety and improvements in the robot's awareness of its environment. Autonomous robots that work in close proximity with humans need vision. They must be able to see to avoid collisions and they use haptic feedback to tell the robot how much force is being exerted on objects. All of these technologies exist, but the largest obstacle to bringing them to the assistive technology market is to prove to the health insurance companies who will fund them that they are both safe and medically necessary. Continue reading →

Posted in Human Robots

#435773 Video Friday: Roller-Skating Quadruped ...

Posted on September 20, 2019 by Android

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!):

IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
RoboBusiness 2019 – October 1-3, 2019 – Santa Clara, CA, USA
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
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today's videos.

We got a sneak peek of a new version of ANYmal equipped with actuated wheels for feet at the DARPA SubT Challenge, where it did surprisingly well at quickly and (mostly) robustly navigating some very tricky terrain. And when you're not expecting it to travel through a muddy, rocky, and dark tunnel, it looks even more capable:

[ Paper ]

Thanks Marko!

In Langley’s makerspace lab, researchers are developing a series of soft robot actuators to investigate the viability of soft robotics in space exploration and assembly. By design, the actuator has chambers, or air bladders, that expand and compress based on the amount of air in them.

[ NASA ]

I’m not normally a fan of the AdultSize RoboCup soccer competition, but NimbRo had a very impressive season.

I don’t know how it managed to not fall over at 45 seconds, but damn.

[ NimbRo ]

This is more AI than robotics, but that’s okay, because it’s totally cool.

I’m wondering whether the hiders ever tried another possibly effective strategy: trapping the seekers in a locked shelter right at the start.

[ OpenAI ]

We haven’t heard much from Piaggio Fast Forward in a while, but evidently they’ve still got a Gita robot going on, designed to be your personal autonomous caddy for absolutely anything that can fit into something the size of a portable cooler.

Available this fall, I guess?

[ Gita ]

This passively triggered robotic hand is startlingly fast, and seems almost predatory when it grabs stuff, especially once they fit it onto a drone.

[ New Dexterity ]

Thanks Fan!

Autonomous vehicles seem like a recent thing, but CMU has been working on them since the mid 1980s.

CMU was also working on drones back before drones were even really a thing:

[ CMU NavLab ] and [ CMU ]

Welcome to the most complicated and expensive robotic ice cream deployment system ever created.

[ Niska ]

Some impressive dexterity from a robot hand equipped with magnetic gears.

[ Ishikawa Senoo Lab ]

The Buddy Arduino social robot kit is now live on Kickstarter, and you can pledge for one of these little dudes for 49 bucks.

[ Kickstarter ]

Thanks Jenny!

Mobile manipulation robots have high potential to support rescue forces in disaster-response missions. Despite the difficulties imposed by real-world scenarios, robots are promising to perform mission tasks from a safe distance. In the CENTAURO project, we developed a disaster-response system which consists of the highly flexible Centauro robot and suitable control interfaces including an immersive telepresence suit and support-operator controls on different levels of autonomy.

[ CENTAURO ]

Thanks Sven!

Determined robots are the cutest robots.

[ Paper ]

The goal of the Dronument project is to create an aerial platform enabling interior and exterior documentation of heritage sites.

It’s got a base station that helps with localization, but still, flying that close to a chandelier in a UNESCO world heritage site makes me nervous.

[ Dronument ]

Thanks Fan!

Avast ye! No hornswaggling, lick-spittlering, or run-rigging over here – Only serious tech for devs. All hands hoay to check out Misty's capabilities and to build your own skills with plenty of heave ho! ARRRRRRRRGH…

International Talk Like a Pirate Day was yesterday, but I'm sure nobody will look at you funny if you keep at it today too.

[ Misty Robotics ]

This video presents an unobtrusive bimanual teleoperation setup with very low weight, consisting of two Vive visual motion trackers and two Myo surface electromyography bracelets. The video demonstrates complex, dexterous teleoperated bimanual daily-living tasks performed by the torque-controlled humanoid robot TORO.

[ DLR RMC ]

Lex Fridman interviews iRobot’s Colin Angle on the Artificial Intelligence Podcast.

Colin Angle is the CEO and co-founder of iRobot, a robotics company that for 29 years has been creating robots that operate successfully in the real world, not as a demo or on a scale of dozens, but on a scale of thousands and millions. As of this year, iRobot has sold more than 25 million robots to consumers, including the Roomba vacuum cleaning robot, the Braava floor mopping robot, and soon the Terra lawn mowing robot. 25 million robots successfully operating autonomously in people's homes to me is an incredible accomplishment of science, engineering, logistics, and all kinds of entrepreneurial innovation.

[ AI Podcast ]

This week’s CMU RI Seminar comes from CMU’s own Sarah Bergbreiter, on Microsystems-Inspired Robotics.

The ability to manufacture micro-scale sensors and actuators has inspired the robotics community for over 30 years. There have been huge success stories; MEMS inertial sensors have enabled an entire market of low-cost, small UAVs. However, the promise of ant-scale robots has largely failed. Ants can move high speeds on surfaces from picnic tables to front lawns, but the few legged microrobots that have walked have done so at slow speeds (< 1 body length/sec) on smooth silicon wafers. In addition, the vision of large numbers of microfabricated sensors interacting directly with the environment has suffered in part due to the brittle materials used in micro-fabrication. This talk will present our progress in the design of sensors, mechanisms, and actuators that utilize new microfabrication processes to incorporate materials with widely varying moduli and functionality to achieve more robustness, dynamic range, and complexity in smaller packages.

[ CMU RI ] Continue reading →

Posted in Human Robots