Tag Archives: better
#437575 AI-Directed Robotic Hand Learns How to ...
Reaching for a nearby object seems like a mindless task, but the action requires a sophisticated neural network that took humans millions of years to evolve. Now, robots are acquiring that same ability using artificial neural networks. In a recent study, a robotic hand “learns” to pick up objects of different shapes and hardness using three different grasping motions.
The key to this development is something called a spiking neuron. Like real neurons in the brain, artificial neurons in a spiking neural network (SNN) fire together to encode and process temporal information. Researchers study SNNs because this approach may yield insights into how biological neural networks function, including our own.
“The programming of humanoid or bio-inspired robots is complex,” says Juan Camilo Vasquez Tieck, a research scientist at FZI Forschungszentrum Informatik in Karlsruhe, Germany. “And classical robotics programming methods are not always suitable to take advantage of their capabilities.”
Conventional robotic systems must perform extensive calculations, Tieck says, to track trajectories and grasp objects. But a robotic system like Tieck’s, which relies on a SNN, first trains its neural net to better model system and object motions. After which it grasps items more autonomously—by adapting to the motion in real-time.
The new robotic system by Tieck and his colleagues uses an existing robotic hand, called a Schunk SVH 5-finger hand, which has the same number of fingers and joints as a human hand.
The researchers incorporated a SNN into their system, which is divided into several sub-networks. One sub-network controls each finger individually, either flexing or extending the finger. Another concerns each type of grasping movement, for example whether the robotic hand will need to do a pinching, spherical or cylindrical movement.
For each finger, a neural circuit detects contact with an object using the currents of the motors and the velocity of the joints. When contact with an object is detected, a controller is activated to regulate how much force the finger exerts.
“This way, the movements of generic grasping motions are adapted to objects with different shapes, stiffness and sizes,” says Tieck. The system can also adapt its grasping motion quickly if the object moves or deforms.
The robotic grasping system is described in a study published October 24 in IEEE Robotics and Automation Letters. The researchers’ robotic hand used its three different grasping motions on objects without knowing their properties. Target objects included a plastic bottle, a soft ball, a tennis ball, a sponge, a rubber duck, different balloons, a pen, and a tissue pack. The researchers found, for one, that pinching motions required more precision than cylindrical or spherical grasping motions.
“For this approach, the next step is to incorporate visual information from event-based cameras and integrate arm motion with SNNs,” says Tieck. “Additionally, we would like to extend the hand with haptic sensors.”
The long-term goal, he says, is to develop “a system that can perform grasping similar to humans, without intensive planning for contact points or intense stability analysis, and [that is] able to adapt to different objects using visual and haptic feedback.” Continue reading →
Posted in Human Robots
#437564 How We Won the DARPA SubT Challenge: ...
This is a guest post. The views expressed here are those of the authors and do not necessarily represent positions of IEEE or its organizational units.
“Do you smell smoke?” It was three days before the qualification deadline for the Virtual Tunnel Circuit of the DARPA Subterranean Challenge Virtual Track, and our team was barrelling through last-minute updates to our robot controllers in a small conference room at the Michigan Tech Research Institute (MTRI) offices in Ann Arbor, Mich. That’s when we noticed the smell. We’d assumed that one of the benefits of entering a virtual disaster competition was that we wouldn’t be exposed to any actual disasters, but equipment in the basement of the building MTRI shares had started to smoke. We evacuated. The fire department showed up. And as soon as we could, the team went back into the building, hunkered down, and tried to make up for the unexpected loss of several critical hours.
Team BARCS joins the SubT Virtual Track
The smoke incident happened more than a year after we first learned of the DARPA Subterranean Challenge. DARPA announced SubT early in 2018, and at that time, we were interested in building internal collaborations on multi-agent autonomy problems, and SubT seemed like the perfect opportunity. Though a few of us had backgrounds in robotics, the majority of our team was new to the field. We knew that submitting a proposal as a largely non-traditional robotics team from an organization not known for research in robotics was a risk. However, the Virtual Track gave us the opportunity to focus on autonomy and multi-agent teaming strategies, areas requiring skill in asynchronous computing and sensor data processing that are strengths of our Institute. The prevalence of open source code, small inexpensive platforms, and customizable sensors has provided the opportunity for experts in fields other than robotics to apply novel approaches to robotics problems. This is precisely what makes the Virtual Track of SubT appealing to us, and since starting SubT, autonomy has developed into a significant research thrust for our Institute. Plus, robots are fun!
After many hours of research, discussion, and collaboration, we submitted our proposal early in 2018. And several months later, we found out that we had won a contract and became a funded team (Team BARCS) in the SubT Virtual Track. Now we needed to actually make our strategy work for the first SubT Tunnel Circuit competition, taking place in August of 2019.
Building a team of virtual robots
A natural approach to robotics competitions like SubT is to start with the question of “what can X-type robot do” and then build a team and strategy around individual capabilities. A particular challenge for the SubT Virtual Track is that we can’t design our own systems; instead, we have to choose from a predefined set of simulated robots and sensors that DARPA provides, based on the real robots used by Systems Track teams. Our approach is to look at what a team of robots can do together, determining experimentally what the best team configuration is for each environment. By the final competition, ideally we will be demonstrating the value of combining platforms across multiple Systems Track teams into a single Virtual Track team. Each of the robot configurations in the competition has an associated cost, and team size is constrained by a total cost. This provides another impetus for limiting dependence on complex sensor packages, though our ranging preference is 3D lidar, which is the most expensive sensor!
Image: Michigan Tech Research Institute
The teams can rely on realistic physics and sensors but they start off with no maps of any kind, so the focus is on developing autonomous exploratory behavior, navigation methods, and object recognition for their simulated robots.
One of the frequent questions we receive about the Virtual Track is if it’s like a video game. While it may look similar on the surface, everything under the hood in a video game is designed to service the game narrative and play experience, not require novel research in AI and autonomy. The purpose of simulations, on the other hand, is to include full physics and sensor models (including noise and errors) to provide a testbed for prototyping and developing solutions to those real-world challenges. We are starting with realistic physics and sensors but no maps of any kind, so the focus is on developing autonomous exploratory behavior, navigation methods, and object recognition for our simulated robots.
Though the simulation is more like real life than a video game, it is not real life. Due to occasional software bugs, there are still non-physical events, like the robots falling through an invisible hole in the world or driving through a rock instead of over it or flipping head over heels when driving over a tiny lip between world tiles. These glitches, while sometimes frustrating, still allow the SubT Virtual platform to be realistic enough to support rapid prototyping of controller modules that will transition straightforwardly onto hardware, closing the loop between simulation and real-world robots.
Full autonomy for DARPA-hard scenarios
The Virtual Track requirement that the robotic agents be fully autonomous, rather than have a human supervisor, is a significant distinction between the Systems and Virtual Tracks of SubT. Our solutions must be hardened against software faults caused by things like missing and bad data since our robots can’t turn to us for help. In order for a team of robots to complete this objective reliably with no human-in-the-loop, all of the internal systems, from perception to navigation to control to actuation to communications, must be able to autonomously identify and manage faults and failures anywhere in the control chain.
The communications limitations in subterranean environments (both real and virtual) mean that we need to keep the amount of information shared between robots low, while making the usability of that information for joint decision-making high. This goal has guided much of our design for autonomous navigation and joint search strategy for our team. For example, instead of sharing the full SLAM map of the environment, our agents only share a simplified graphical representation of the space, along with data about frontiers it has not yet explored, and are able to merge its information with the graphs generated by other agents. The merged graph can then be used for planning and navigation without having full knowledge of the detailed 3D map.
The Virtual Track requires that the robotic agents be fully autonomous. With no human-in-the-loop, all of the internal systems, from perception to navigation to control to actuation to communications, must be able to identify and manage faults and failures anywhere in the control chain.
Since the objective of the SubT program is to advance the state-of-the-art in rapid autonomous exploration and mapping of subterranean environments by robots, our first software design choices focused on the mapping task. The SubT virtual environments are sufficiently rich as to provide interesting problems in building so-called costmaps that accurately separate obstructions that are traversable (like ramps) from legitimately impassible obstructions. An extra complication we discovered in the first course, which took place in mining tunnels, was that the angle of the lowest beam of the lidar was parallel to the down ramps in the tunnel environment, so they could not “see” the ground (or sometimes even obstructions on the ramp) until they got close enough to the lip of the ramp to receive lidar reflections off the bottom of the ramp. In this case, we had to not only change the costmap to convince the robot that there was safe ground to reach over the lip of the ramp, but also had to change the path planner to get the robot to proceed with caution onto the top of the ramp in case there were previously unseen obstructions on the ramp.
In addition to navigation in the costmaps, the robot must be able to generate its own goals to navigate to. This is what produces exploratory behavior when there is no map to start with. SLAM is used to generate a detailed map of the environment explored by a single robot—the space it has probed with its sensors. From the sensor data, we are able to extract information about the interior space of the environment while looking for holes in the data, to determine things like whether the current tunnel continues or ends, or how many tunnels meet at an intersection. Once we have some understanding of the interior space, we can place navigation goals in that space. These goals naturally update as the robot traverses the tunnel, allowing the entire space to be explored.
Sending our robots into the virtual unknown
The solutions for the Virtual Track competitions are tested by DARPA in multiple sequestered runs across many environments for each Circuit in the month prior to the Systems Track competition. We must wait until the joint award ceremony at the conclusion of the Systems Track to find out the results, and we are completely in the dark about placings before the awards are announced. It’s nerve-wracking! The challenges of the worlds used in the Circuit events are also hand-designed, so features of the worlds we use for development could be combined in ways we have not anticipated—it’s always interesting to see what features were prioritized after the event. We test everything in our controllers well enough to feel confident that we at least are submitting something reasonably stable and broadly capable, and once the solution is in, we can’t really do anything other than “let go” and get back to work on the next phase of development. Maybe it’s somewhat like sending your kid to college: “we did our best to prepare you for this world, little bots. Go do good.”
Image: Michigan Tech Research Institute
The first SubT competition was the Tunnel Circuit, featuring a labyrinthine environment that simulated human-engineered tunnels, including hazards such as vertical shafts and rubble.
The first competition was the Tunnel Circuit, in October 2019. This environment models human-engineered tunnels. Two substantial challenges in this environment were vertical shafts and rubble. Our team accrued 21 points over 15 competition runs in five separate tunnel environments for a second place finish, behind Team Coordinated Robotics.
The next phase of the SubT virtual competition was the Urban Circuit. Much of the difference between our Tunnel and Urban Circuit results came down to thorough testing to identify failure modes and implementations of checks and data filtering for fault tolerance. For example, in the SLAM nodes run by a single robot, the coordinates of the most recent sensor data are changed multiple times during processing and integration into the current global 3D map of the “visited” environment stored by that robot. If there is lag in IMU or clock data, the observation may be temporarily registered at a default location that is very far from the actual position. Since most of our decision processes for exploration are downstream from SLAM, this can cause faulty or impossible goals to be generated, and the robots then spend inordinate amounts of time trying to drive through walls. We updated our method to add a check to see if the new map position has jumped a far distance from the prior map position, and if so, we threw that data out.
Image: Michigan Tech Research Institute
In open spaces like the rooms in the Urban circuit, we adjusted our approach to exploration through graph generation to allow the robots to accurately identify viable routes while helping to prevent forays off platform edges.
Our approach to exploration through graph generation based on identification of interior spaces allowed us to thoroughly explore the centers of rooms, although we did have to make some changes from the Tunnel circuit to achieve that. In the Tunnel circuit, we used a simplified graph of the environment based on landmarks like intersections. The advantage of this approach is that it is straightforward for two robots to compare how the graphs of the space they explored individually overlap. In open spaces like the rooms in the Urban circuit, we chose to instead use a more complex, less directly comparable graph structure based on the individual robot’s trajectory. This allowed the robots to accurately identify viable routes between features like subway station platforms and subway tracks, as well as to build up the navigation space for room interiors, while helping to prevent forays off the platform edges. Frontier information is also integrated into the graph, providing a uniform data structure for both goal selection and route planning.
The results are in!
The award ceremony for the Urban Circuit was held concurrently with the Systems Track competition awards this past February in Washington State. We sent a team representative to participate in the Technical Interchange Meeting and present the approach for our team, and the rest of us followed along from our office space on the DARPAtv live stream. While we were confident in our solution, we had also been tracking the online leaderboard and knew our competitors were going to be submitting strong solutions. Since the competition environments are hand-designed, there are always novel challenges that could be presented in these environments as well. We knew we would put up a good fight, but it was very exciting to see BARCS appear in first place!
Any time we implement a new module in our control system, there is a lot of parameter tuning that has to happen to produce reliably good autonomous behavior. In the Urban Circuit, we did not sufficiently test some parameter values in our exploration modules. The effect of this was that the robots only chose to go down small hallways after they explored everything else in their environment, which meant very often they ran out of time and missed a lot of small rooms. This may be the biggest source of lost points for us in the Urban Circuit. One of our major plans going forward from the Urban Circuit is to integrate more sophisticated node selection methods, which can help our robots more intelligently prioritize which frontier nodes to visit. By going through all three Circuit challenges, we will learn how to appropriately add weights to the frontiers based on features of the individual environments. For the Final Challenge, when all three Circuit environments will be combined into large systems, we plan to implement adaptive controllers that will identify their environments and use the appropriate optimized parameters for that environment. In this way, we expect our agents to be able to (for example) prioritize hallways and other small spaces in Urban environments, and perhaps prioritize large openings over small in the Cave environments, if the small openings end up being treacherous overall.
Next for our team: Cave Circuit
Coming up next for Team BARCS is the Virtual Cave Circuit. We are in the middle of testing our hypothesis that our controller will transition from UGVs to UAVs and developing strategies for refining our solution to handle Cave Circuit environmental hazards. The UAVs have a shorter battery life than the UGVs, so executing a joint exploration strategy will also be a high priority for this event, as will completing our work on graph sharing and merging, which will give our robot teams more sophisticated options for navigation and teamwork. We’re reaching a threshold in development where we can start increasing the “smarts” of the robots, which we anticipate will be critical for the final competition, where all of the challenges of SubT will be combined to push the limits of innovation. The Cave Circuit will also have new environmental challenges to tackle: dynamic features such as rock falls have been added, which will block previously accessible passages in the cave environment. We think our controllers are well-poised to handle this new challenge, and we’re eager to find out if that’s the case.
As of now, the biggest worries for us are time and team composition. The Cave Circuit deadline has been postponed to October 15 due to COVID-19 delays, with the award ceremony in mid-November, but there have also been several very compelling additions to the testbed that we would like to experiment with before submission, including droppable networking ‘breadcrumbs’ and new simulated platforms. There are design trade-offs when balancing general versus specialist approaches to the controllers for these robots—since we are adding UAVs to our team for the first time, there are new decisions that will have to be made. For example, the UAVs can ascend into vertical spaces, but only have a battery life of 20 minutes. The UGVs by contrast have 90 minute battery life. One of our strategies is to do an early return to base with one or more agents to buy down risk on making any artifact reports at all for the run, hedging against our other robots not making it back in time, a lesson learned from the Tunnel Circuit. Should a UAV take on this role, or is it better to have them explore deeper into the environment and instead report their artifacts to a UGV or network node, which comes with its own risks? Testing and experimentation to determine the best options takes time, which is always a worry when preparing for a competition! We also anticipate new competitors and stiffer competition all around.
Image: Michigan Tech Research Institute
Team BARCS has now a year to prepare for the final DARPA SubT Challenge event, expected to take place in late 2021.
Going forward from the Cave Circuit, we will have a year to prepare for the final DARPA SubT Challenge event, expected to take place in late 2021. What we are most excited about is increasing the level of intelligence of the agents in their teamwork and joint exploration of the environment. Since we will have (hopefully) built up robust approaches to handling each of the specific types of environments in the Tunnel, Urban, and Cave circuits, we will be aiming to push the limits on collaboration and efficiency among the agents in our team. We view this as a central research contribution of the Virtual Track to the Subterranean Challenge because intelligent, adaptive, multi-robot collaboration is an upcoming stage of development for integration of robots into our lives.
The Subterranean Challenge Virtual Track gives us a bridge for transitioning our more abstract research ideas and algorithms relevant to this degree of autonomy and collaboration onto physical systems, and exploring the tangible outcomes of implementing our work in the real world. And the next time there’s an incident in the basement of our building, the robots (and humans) of Team BARCS will be ready to respond.
Richard Chase, Ph.D., P.E., is a research scientist at Michigan Tech Research Institute (MTRI) and has 20 years of experience developing robotics and cyber physical systems in areas from remote sensing to autonomous vehicles. At MTRI, he works on a variety of topics such as swarm autonomy, human-swarm teaming, and autonomous vehicles. His research interests are the intersection of design, robotics, and embedded systems.
Sarah Kitchen is a Ph.D. mathematician working as a research scientist and an AI/Robotics focus area leader at MTRI. Her research interests include intelligent autonomous agents and multi-agent collaborative teams, as well as applications of autonomous robots to sensing systems.
This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001118C0124 and is released under Distribution Statement (Approved for Public Release, Distribution Unlimited). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA. Continue reading →
Posted in Human Robots
#437562 Video Friday: Aquanaut Robot Takes to ...
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!):
IROS 2020 – October 25-25, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Bay Area Robotics Symposium – November 20, 2020 – [Online]
ACRA 2020 – December 8-10, 2020 – [Online]
Let us know if you have suggestions for next week, and enjoy today's videos.
To prepare the Perseverance rover for its date with Mars, NASA’s Mars 2020 mission team conducted a wide array of tests to help ensure a successful entry, descent and landing at the Red Planet. From parachute verification in the world’s largest wind tunnel, to hazard avoidance practice in Death Valley, California, to wheel drop testing at NASA’s Jet Propulsion Laboratory and much more, every system was put through its paces to get ready for the big day. The Perseverance rover is scheduled to land on Mars on February 18, 2021.
[ JPL ]
Awesome to see Aquanaut—the “underwater transformer” we wrote about last year—take to the ocean!
Also their new website has SHARKS on it.
[ HMI ]
Nature has inspired engineers at UNSW Sydney to develop a soft fabric robotic gripper which behaves like an elephant's trunk to grasp, pick up and release objects without breaking them.
[ UNSW ]
Collaborative robots offer increased interaction capabilities at relatively low cost but, in contrast to their industrial counterparts, they inevitably lack precision. We address this problem by relying on a dual-arm system with laser-based sensing to measure relative poses between objects of interest and compensate for pose errors coming from robot proprioception.
[ Paper ]
Developed by NAVER LABS, with Korea University of Technology & Education (Koreatech), the robot arm now features an added waist, extending the available workspace, as well as a sensor head that can perceive objects. It has also been equipped with a robot hand “BLT Gripper” that can change to various grasping methods.
[ NAVER Labs ]
In case you were still wondering why SoftBank acquired Aldebaran and Boston Dynamics:
[ RobotStart ]
DJI's new Mini 2 drone is here with a commercial so hip it makes my teeth scream.
[ DJI ]
Using simple materials, such as plastic struts and cardboard rolls, the first prototype of the RBO Hand 3 is already capable of grasping a large range of different objects thanks to its opposable thumb.
The RBO Hand 3 performs an edge grasp before handing-over the object to a person. The hand actively exploits constraints in the environment (the tabletop) for grasping the object. Thanks to its compliance, this interaction is safe and robust.
[ TU Berlin ]
Flyability's Elios 2 helped researchers inspect Reactor Five at the Chernobyl nuclear disaster site in order to determine whether any uranium was present. Prior to this mission, Reactor Five had not been investigated since the disaster in April of 1986.
[ Flyability ]
Thanks Zacc!
SOTO 2 is here! Together with our development partners from the industry, we have greatly enhanced the SOTO prototype over the last two years. With the new version of the robot, Industry 4.0 will become a great deal more real: SOTO brings materials to the assembly line, just-in-time and completely autonomously.
[ Magazino ]
A drone that can fly sustainably for long distances over land and water, and can land almost anywhere, will be able to serve a wide range of applications. There are already drones that fly using ‘green’ hydrogen, but they either fly very slowly or cannot land vertically. That’s why researchers at TU Delft, together with the Royal Netherlands Navy and the Netherlands Coastguard, developed a hydrogen-powered drone that is capable of vertical take-off and landing whilst also being able to fly horizontally efficiently for several hours, much like regular aircraft. The drone uses a combination of hydrogen and batteries as its power source.
[ MAVLab ]
The National Nuclear User Facility for Hot Robotics (NNUF-HR) is an EPSRC funded facility to support UK academia and industry to deliver ground-breaking, impactful research in robotics and artificial intelligence for application in extreme and challenging nuclear environments.
[ NNUF ]
At the Karolinska University Laboratory in Sweden, an innovation project based around an ABB collaborative robot has increased efficiency and created a better working environment for lab staff.
[ ABB ]
What I find interesting about DJI's enormous new agricultural drone is that it's got a spinning obstacle detecting sensor that's a radar, not a lidar.
Also worth noting is that it seems to detect the telephone pole, but not the support wire that you can see in the video feed, although the visualization does make it seem like it can spot the power lines above.
[ DJI ]
Josh Pieper has spend the last year building his own quadruped, and you can see what he's been up to in just 12 minutes.
[ mjbots ]
Thanks Josh!
Dr. Ryan Eustice, TRI Senior Vice President of Automated Driving, delivers a keynote speech — “The Road to Vehicle Automation, a Toyota Guardian Approach” — to SPIE's Future Sensing Technologies 2020. During the presentation, Eustice provides his perspective on the current state of automated driving, summarizes TRI's Guardian approach — which amplifies human drivers, rather than replacing them — and summarizes TRI's recent developments in core AD capabilities.
[ TRI ]
Two excellent talks this week from UPenn GRASP Lab, from Ruzena Bajcsy and Vijay Kumar.
A panel discussion on the future of robotics and societal challenges with Dr. Ruzena Bajcsy as a Roboticist and Founder of the GRASP Lab.
In this talk I will describe the role of the White House Office of Science and Technology Policy in supporting science and technology research and education, and the lessons I learned while serving in the office. I will also identify a few opportunities at the intersection of technology and policy and broad societal challenges.
[ UPenn ]
The IROS 2020 “Perception, Learning, and Control for Autonomous Agile Vehicles” workshop is all online—here's the intro, but you can click through for a playlist that includes videos of the entire program, and slides are available as well.
[ NYU ] Continue reading →
Posted in Human Robots
#437550 McDonald’s Is Making a Plant-Based ...
Fast-food chains have been doing what they can in recent years to health-ify their menus. For better or worse, burgers, fries, fried chicken, roast beef sandwiches, and the like will never go out of style—this is America, after all—but consumers are increasingly gravitating towards healthier options.
One of those options is plant-based foods, and not just salads and veggie burgers, but “meat” made from plants. Burger King was one of the first big fast-food chains to jump on the plant-based meat bandwagon, introducing its Impossible Whopper in restaurants across the country last year after a successful pilot program. Dunkin’ (formerly Dunkin’ Donuts) uses plant-based patties in its Beyond Sausage breakfast sandwiches.
But there’s one big player in the fast food market that’s been oddly missing from the plant-based trend—until now. McDonald’s announced last week that it will debut a sandwich called the McPlant in key US markets next year. Unlike Dunkin’ and Burger King, who both worked with Impossible Foods to make their plant-based products, McDonald’s worked with Los Angeles-based Beyond Meat, which makes chicken, beef, and pork-like products from plants.
According to Bloomberg, though, McDonald’s decided to forego a partnership with Beyond Meat in favor of creating its own plant-based products. Imitation chicken nuggets and plant-based breakfast sandwiches are in its plans as well.
McDonald’s has bounced back impressively from its March low (when the coronavirus lockdowns first happened in the US). Last month the company’s stock reached a 52-week high of $231 per share (as compared to its low in March of $124 per share).
To keep those numbers high and make it as easy as possible for customers to get their hands on plant-based burgers and all the traditional menu items too, the fast food chain is investing in tech and integrating more digital offerings into its restaurants.
McDonald’s has acquired a couple artificial intelligence companies in the last year and a half; Dynamic Yield is an Israeli company that uses AI to personalize customers’ experiences, and McDonald’s is using Dynamic Yield’s tech on its smart menu boards, for example by customizing the items displayed on the drive-thru menu based on the weather and the time of day, and recommending additional items based on what a customer asks for first (i.e. “You know what would go great with that coffee? Some pancakes!”).
The fast food giant also bought Apprente, a startup that uses AI in voice-based ordering platforms. McDonald’s is using the tech to help automate its drive-throughs.
In addition to these investments, the company plans to launch a digital hub called MyMcDonald’s that will include a loyalty program, start doing deliveries of its food through its mobile app, and test different ways of streamlining the food order and pickup process—with many of the new ideas geared towards pandemic times, like express pickup lanes for people who placed digital orders and restaurants with drive-throughs for delivery and pickup orders only.
Plant-based meat patties appear to be just one small piece of McDonald’s modernization plans. Those of us who were wondering what they were waiting for should have known—one of the most-recognized fast food chains in the world wasn’t about to let itself get phased out. It seems it will only be a matter of time until you can pull out your phone, make a few selections, and have a burger made from plants—with a side of fries made from more plants—show up at your door a little while later. Drive-throughs, shouting your order into a fuzzy speaker with a confused teen on the other end, and burgers made from beef? So 2019.
Image Credit: McDonald’s Continue reading →
Posted in Human Robots
#437543 This Is How We’ll Engineer Artificial ...
Take a Jeopardy! guess: this body part was once referred to as the “consummation of all perfection as an instrument.”
Answer: “What is the human hand?”
Our hands are insanely complex feats of evolutionary engineering. Densely-packed sensors provide intricate and ultra-sensitive feelings of touch. Dozens of joints synergize to give us remarkable dexterity. A “sixth sense” awareness of where our hands are in space connects them to the mind, making it possible to open a door, pick up a mug, and pour coffee in total darkness based solely on what they feel.
So why can’t robots do the same?
In a new article in Science, Dr. Subramanian Sundaram at Boston and Harvard University argues that it’s high time to rethink robotic touch. Scientists have long dreamed of artificially engineering robotic hands with the same dexterity and feedback that we have. Now, after decades, we’re at the precipice of a breakthrough thanks to two major advances. One, we better understand how touch works in humans. Two, we have the mega computational powerhouse called machine learning to recapitulate biology in silicon.
Robotic hands with a sense of touch—and the AI brain to match it—could overhaul our idea of robots. Rather than charming, if somewhat clumsy, novelties, robots equipped with human-like hands are far more capable of routine tasks—making food, folding laundry—and specialized missions like surgery or rescue. But machines aren’t the only ones to gain. For humans, robotic prosthetic hands equipped with accurate, sensitive, and high-resolution artificial touch is the next giant breakthrough to seamlessly link a biological brain to a mechanical hand.
Here’s what Sundaram laid out to get us to that future.
How Does Touch Work, Anyway?
Let me start with some bad news: reverse engineering the human hand is really hard. It’s jam-packed with over 17,000 sensors tuned to mechanical forces alone, not to mention sensors for temperature and pain. These force “receptors” rely on physical distortions—bending, stretching, curling—to signal to the brain.
The good news? We now have a far clearer picture of how biological touch works. Imagine a coin pressed into your palm. The sensors embedded in the skin, called mechanoreceptors, capture that pressure, and “translate” it into electrical signals. These signals pulse through the nerves on your hand to the spine, and eventually make their way to the brain, where they gets interpreted as “touch.”
At least, that’s the simple version, but one too vague and not particularly useful for recapitulating touch. To get there, we need to zoom in.
The cells on your hand that collect touch signals, called tactile “first order” neurons (enter Star Wars joke) are like upside-down trees. Intricate branches extend from their bodies, buried deep in the skin, to a vast area of the hand. Each neuron has its own little domain called “receptor fields,” although some overlap. Like governors, these neurons manage a semi-dedicated region, so that any signal they transfer to the higher-ups—spinal cord and brain—is actually integrated from multiple sensors across a large distance.
It gets more intricate. The skin itself is a living entity that can regulate its own mechanical senses through hydration. Sweat, for example, softens the skin, which changes how it interacts with surrounding objects. Ever tried putting a glove onto a sweaty hand? It’s far more of a struggle than a dry one, and feels different.
In a way, the hand’s tactile neurons play a game of Morse Code. Through different frequencies of electrical beeps, they’re able to transfer information about an object’s size, texture, weight, and other properties, while also asking the brain for feedback to better control the object.
Biology to Machine
Reworking all of our hands’ greatest features into machines is absolutely daunting. But robots have a leg up—they’re not restricted to biological hardware. Earlier this year, for example, a team from Columbia engineered a “feeling” robotic finger using overlapping light emitters and sensors in a way loosely similar to receptor fields. Distortions in light were then analyzed with deep learning to translate into contact location and force.
Although a radical departure from our own electrical-based system, the Columbia team’s attempt was clearly based on human biology. They’re not alone. “Substantial progress is being made in the creation of soft, stretchable electronic skins,” said Sundaram, many of which can sense forces or pressure, although they’re currently still limited.
What’s promising, however, is the “exciting progress in using visual data,” said Sundaram. Computer vision has gained enormously from ubiquitous cameras and large datasets, making it possible to train powerful but data-hungry algorithms such as deep convolutional neural networks (CNNs).
By piggybacking on their success, we can essentially add “eyes” to robotic hands, a superpower us humans can’t imagine. Even better, CNNs and other classes of algorithms can be readily adopted for processing tactile data. Together, a robotic hand could use its eyes to scan an object, plan its movements for grasp, and use touch for feedback to adjust its grip. Maybe we’ll finally have a robot that easily rescues the phone sadly dropped into a composting toilet. Or something much grander to benefit humanity.
That said, relying too heavily on vision could also be a downfall. Take a robot that scans a wide area of rubble for signs of life during a disaster response. If touch relies on sight, then it would have to keep a continuous line-of-sight in a complex and dynamic setting—something computer vision doesn’t do well in, at least for now.
A Neuromorphic Way Forward
Too Debbie Downer? I got your back! It’s hard to overstate the challenges, but what’s clear is that emerging machine learning tools can tackle data processing challenges. For vision, it’s distilling complex images into “actionable control policies,” said Sundaram. For touch, it’s easy to imagine the same. Couple the two together, and that’s a robotic super-hand in the making.
Going forward, argues Sundaram, we need to closely adhere to how the hand and brain process touch. Hijacking our biological “touch machinery” has already proved useful. In 2019, one team used a nerve-machine interface for amputees to control a robotic arm—the DEKA LUKE arm—and sense what the limb and attached hand were feeling. Pressure on the LUKE arm and hand activated an implanted neural interface, which zapped remaining nerves in a way that the brain processes as touch. When the AI analyzed pressure data similar to biological tactile neurons, the person was able to better identify different objects with their eyes closed.
“Neuromorphic tactile hardware (and software) advances will strongly influence the future of bionic prostheses—a compelling application of robotic hands,” said Sundaram, adding that the next step is to increase the density of sensors.
Two additional themes made the list of progressing towards a cyborg future. One is longevity, in that sensors on a robot need to be able to reliably produce large quantities of high-quality data—something that’s seemingly mundane, but is a practical limitation.
The other is going all-in-one. Rather than just a pressure sensor, we need something that captures the myriad of touch sensations. From feather-light to a heavy punch, from vibrations to temperatures, a tree-like architecture similar to our hands would help organize, integrate, and otherwise process data collected from those sensors.
Just a decade ago, mind-controlled robotics were considered a blue sky, stretch-goal neurotechnological fantasy. We now have a chance to “close the loop,” from thought to movement to touch and back to thought, and make some badass robots along the way.
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