Unitree launched its first humanoid robot H1 on August 15. Showcasing six months of hard work, the H1 humanoid stands about 71 inches (1800mm) tall, but only weighs about 100 lbs (47kg) and boasts unmatched power.

Meet the Unitree H1: a humanoid general-purpose robot starts a new industrial revolution!

On 12 July, Unitree unveiled its latest quadruped robot - the Unitree Go2, which features embodied AI technology, standard 4D ultra-wide LiDAR and GPT empowerment. This product is expected to revolutionize the consumer-grade quadruped robots industry, leading to a transformation of the sector.  

Revolution: A New Quadruped Robot of Embodied AI

On the Moon, there are raw materials that humanity could one day mine and use. Various space agencies, such as the European Space Agency (ESA), are already planning missions to better explore Earth&rsquo;s satellite and find minerals. This calls for appropriate exploration vehicles. Swiss researchers led by ETH Zurich are now pursuing the idea of sending not just one solitary rover on an exploration tour, but rather an entire team of vehicles and flying devices that complement each other.The researchers equipped three ANYmal &ndash; a type of legged robot developed at ETH &ndash; with a range of measuring and analysis instruments that would potentially make them suitable exploration devices in the future. They tested these robots on various terrains in Switzerland and at the European Space Resources Innovation Centre (ESRIC) in Luxembourg, where, a few months ago, the Swiss team won a European competition for lunar exploration robots together with colleagues from Germany. The competition involved finding and identifying minerals on a test site modelled after the surface of the Moon. In the latest issue of the journal <a href="https://doi.org/10.1126/scirobotics.ade9548" target="_blank">Science Robotics</a>, the scientists describe how they go about exploring an unknown terrain using a team of robots.<h2>Insurance against failure</h2>&ldquo;Using multiple robots has two advantages,&rdquo; explains Philip Arm, a doctoral student in the group led by ETH Professor Marco Hutter. &ldquo;The individual robots can take on specialised tasks and perform them simultaneously. Moreover, thanks to its redundancy, a robot team is able to compensate for a teammate&rsquo;s failure.&rdquo; Redundancy in this case means that important measuring equipment is installed on several robots. In other words, redundancy and specialisation are opposing goals. &ldquo;Getting the benefits of both is a matter of finding the right balance,&rdquo; Arm says.<iframe width="640" height="360" src="https://www.youtube.com/embed/bqwbQzVrzkQ?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>(Video: ETH Zurich with footage from the University of Zurich / IT Services, MELS)&nbsp;The researchers at ETH Zurich and the Universities of Basel, Bern and Zurich solved this problem by equipping two of the legged robots as specialists. One robot was programmed to be particularly good at mapping the terrain and classifying the geology. It used a laser scanner and several cameras &ndash; some of them capable of spectral analysis &ndash; to gather initial clues about the mineral composition of the rock. The other specialist robot was taught to precisely identify rocks using a Raman spectrometer and a microscopy camera.The third robot was a generalist: it was able to both map the terrain and identify rocks, which meant that it had a broader range of tasks than the specialists. However, its equipment meant that it could perform these tasks with less precision. &ldquo;This makes it possible to complete the mission should any one of the robots malfunction,&rdquo; Arm says.<h2>Combination is key</h2>At the ESRIC and ESA Space Resources Challenge, the jury was particularly impressed that the researchers had built redundancy into their exploration system to make it resilient to potential failures. As a prize, the Swiss scientists and their colleagues from the FZI Research Center for Information Technology in Karlsruhe, were awarded a one-year research contract to further develop this technology. In addition to legged robots, this work will also involve robots with wheels, building on the FZI researchers&rsquo; experience with such robots.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1689324569825-image.imageformat.1286.1151136034.jpg" style="width: 766px;" class="fr-fic fr-dib">The Swiss team with one of their legged robots and astronaut Matthias Maurer (centre) at the Space Resources Challenge. (Photograph: ETH Zurich)&nbsp;&ldquo;Legged robots like our ANYmal cope well in rocky and steep terrain, for example when it comes to climbing down into a crater,&rdquo; explains Hendrik Kolvenbach, a senior scientist in Professor Hutter&rsquo;s group. Robots with wheels are at a disadvantage in these kinds of conditions, but they can move faster on less challenging terrain. For a future mission, it would therefore make sense to combine robots that differ in terms of their mode of locomotion. Flying robots could also be added to the team.The researchers also plan to make the robots more autonomous. Presently, all data from the robots flows into a control centre, where an operator assigns tasks to the individual robots. In the future, semi-autonomous robots could directly assign certain tasks to each other, with control and intervention options for the operator.<hr><h2 id="isPasted">Reference</h2>Arm P, Waibel G, Preisig J, Tuna T, Zhou R, Bickel V, Ligeza G, Miki T, Kehl F, Kolvenbach H, Hutter M: Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots. Science Robotics, 12 June 2023, doi: <a href="https://doi.org/10.1126/scirobotics.ade9548" target="_blank">10.1126/scirobotics.ade9548</a>

A team is greater than the sum of its parts – the trio of legged robots during a test in a Swiss gravel quarry. (Photograph: ETH Zurich / Takahiro Miki)

Swiss engineers are training legged robots for future lunar missions that will search for minerals and raw materials. To ensure that the robots can continue to work even if one of them malfunctions, the researchers are teaching them teamwork.

Robot team on lunar exploration tour

<h3 id="isPasted"><iframe width="640" height="360" src="https://www.youtube.com/embed/Qozjbn0nE-I?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></h3><h3>Background</h3>Cable tunnels are essential for urban power transmission, especially for ultra-high-voltage cables requiring complex networks and flexible layouts. However, cable tunnels also face many challenges in terms of construction and maintenance, such as seepage, gas explosion, dust pollution, and partial discharge.&nbsp;Traditional cable tunnel inspection methods are labour-intensive, time-consuming, subjective, and unsafe. They usually involve many trained operators to conduct regular inspections for hours, shortening the time for analyzing reports for the day. Moreover, the operators have to enter the tunnel environment that may be full of poisonous gases, high-voltage cables, and other electrical equipment, which greatly threatens their health and safety.Therefore, there is an urgent need for a new technology that can improve the efficiency, accuracy, and safety of cable tunnel inspection. <h3 id="isPasted"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg5MDY1NTc3NjMxLURlZXAgUm9ib3RpY3MgMS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">X20 has the capability to traverse rugged terrain Solution</h3>DEEP Robotics is a leading company in the field of quadruped robot industrial applications in China, which cooperates with Jiangsu Mingyue Software Technology to develop an intelligent cable tunnel inspection system that integrates various detection methods such as partial discharge, temperature detection, video monitoring, and defect warning. The system uses a four-legged robot equipped with partial discharge sensors on the robotic arm and a Bi-spectrum camera to inspect multiple cables along a planned route in the tunnel environment and upload the data in real-time. The system can greatly reduce the number of times and frequency of inspection personnel entering the cable tunnel and improve maintenance efficiency and safety.The X20 robot dog can perform automatic inspection tasks according to the preset path and tasks. During the inspection, the robot uses LiDAR and depth cameras to detect the terrain and can turn around, climb stairs and obstacle avoidance. The six-degree-of-freedom robotic arm can flexibly perform cable detection work with trajectory restorations in a narrow tunnel space.The partial discharge detection device can detect discharge signals from 0 MHz to 100 MHz, and accurately identify surface discharge, corona discharge and internal discharge of cables within 200 meters, providing early warning of risks to ensure safe operation.&nbsp;The Bi-spectrum camera features video monitoring and infrared temperature scouting for video and photo capture, identifying temperature differences with an AI algorithm to find out overheated points for early warning.All collected data can be transmitted to the server in real-time through network devices and timely synchronize information with the staff responsible for monitoring in the background, providing data support for further decision analysis for maintenance personnel. <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg5MDY1NjU1NTgwLURlZXAgUm9ib3RpY3MgMy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">Partial discharge detection with a robotic arm <h3>Benefits</h3>The X20 intelligent cable tunnel inspection system with mobile partial discharge detection has brought many benefits to the power industry, such as:&bull; &nbsp; &nbsp;Improving the efficiency of cable tunnel inspection by reducing the time of inspection and data analysis.&bull; &nbsp; &nbsp;Improving the accuracy of cable tunnel inspection by using objective and quantitative data from multiple sensors instead of relying on subjective and qualitative judgments from human operators.&bull; &nbsp; &nbsp;Improving the safety of cable tunnel inspection by minimizing the exposure of human operators to hazardous environments such as poisonous gases, high-voltage cables, and other electrical equipment.&bull; &nbsp; &nbsp;Improving the reliability and safety of the power supply by detecting and preventing potential faults caused by partial discharge in advance, avoiding fire or breakdown accidents.

The X20 robot dog can perform automatic inspection tasks according to the preset path and tasks. During the inspection, the robot uses LiDAR and depth cameras to detect the terrain, and has the ability to turn around, climb stairs, and obstacle avoidance. 

How X20 Robot Dog Intelligent Cable Tunnel Inspection System Complete Mobile Detection

One of the newest versions of its dexterous intelligent robot dog, Lite3, has now launched in Europe. It combines advanced mobility and an open modular structure for education, research and innovative entertainment.&nbsp;<iframe width="640" height="360" src="https://www.youtube.com/embed/NNkxlKLMoMM?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>The new Lite3 series offers four product models: a basic model designed for technology enthusiasts; and three more advanced versions intended for scientific study purposes. With new features that include DEEP Robotics&rsquo; latest proprietary limb joints, control systems and advanced algorithms, Lite3 has a more powerful, quick and durable movement capability. It also offers robust scalability that can be ramped up by installing more modules.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1685346634643-1E57E160-8E6D-4B25-BF27-ED02B809BE3D-11136-000001DE250C2148.png" style="width: 300px;" class="fr-fic fr-dib">Lite3 product parametersMajor design upgradeThe new version incorporates significant technological advances on Lite2. The latest robot dog has proprietary leg-joint technology that provides far greater strength and drive, with torque increased by 50%. The high-torque joint drive module offers extremely high torque density, response bandwidth and reversed transmission efficiency, enhanced rotation stability, and less power consumption.&nbsp;The payload of Lite3 is 40% greater than that of its predecessor. It can carry loads of up to 7.5 kilograms. Its endurance in terms of time and range has nearly doubled. The Lite3 can operate for up to 90 minutes and has a range of 5km.&nbsp;The Lite3 features an industrial-level real-time control system with depth optimization, a first for the product lineup, with overall computing power increased threefold. The Lite 3 can climb steps up to 15 cm in height, close to the current maximum for a quadruped robot of its size, and runs at a peak speed of 4ms-1. It can also perform horizontal jumps, high jumps, front flips and other similarly difficult manoeuvres. The algorithm upgrades make its movement more agile and responsive. <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0ODIzNjcyNTA5LTMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 516px;" class="fr-fic fr-dib">Lite 3: Advanced mobilities of high jumps, horizontal jumps, and front flips<h3 id="isPasted">How can quadruped robots help with academic research?</h3>Quadruped robots can be beneficial for academic research. They differ from two-legged, wheeled or tracked robots because they can navigate multiple terrains and perform high-risk tasks without endangering human life. Quadruped robots can be used in various fields, such as locomotion and biomechanics studies, refining robotics algorithms and control systems, artificial intelligence and machine learning, and human-robot interaction. These robots can provide researchers with valuable insights and help advance scientific knowledge across multiple disciplines.DEEP Robotics&rsquo; robot dogs are already used by some universities, including Tsinghua, Peking and Edinburgh, and University College London. The new Lite3 is positioned to be affordable for research purposes as well as for robotics enthusiasts.<h3>Open modular structure</h3>The open modular structure and interfaces of the Lite3 make it highly scalable and flexible in its capabilities. Modules such as ones for Real-Time Kinematic (RTK), artificial intelligence, edge processing and other types of sensors can be readily added.&nbsp;To enhance its navigation across very challenging terrain, the Lite3 provides levelled&ndash;perception capability interfaces (SDK, API) combined with LiDAR (Light Detection and Ranging) and depth camera to develop auto-navigation, auto-stop, obstacle avoidance, visual positioning, 3D scanning and other perceptional abilities.&nbsp; To find out more about where you can purchase a Lite3 quadruped robot, visit our <a href="https://www.deeprobotics.cn/en/index/product1.html" rel="noopener noreferrer" target="_blank">website</a>.

Lite3: new technology for increased strength, agility, mobility, speed, navigation, and endurance

Advanced mobility and modular functions allow complex moves such as front flipping.

Quadruped robot with advanced front flipping mobility

Jueying X20 can complete unmanned detection and rescue tasks in harsh conditions

With the flexibility to deliver unmanned detection and rescue services, Jueying X20 is designed for the complex terrain of a post-earthquake landscape, the insides of vulnerable debris buildings, tunnel traffic accidents, and in toxic, hypoxia, and high-density smoke environments created by chemical pollution or disaster event.

Jueying X20 Robot Dog Hazard Detection Rescue Solution

This article was first published on&nbsp;<a href="https://news.mit.edu/2023/legged-robotic-system-playing-soccer-various-terrains-0403" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>. &nbsp;<hr>If you&#39;ve ever played soccer with a robot, it&#39;s a familiar feeling. Sun glistens down on your face as the smell of grass permeates the air. You look around. A four-legged robot is hustling toward you, dribbling with determination.&nbsp;While the bot doesn&rsquo;t display a Lionel Messi-like level of ability, it&#39;s an impressive in-the-wild dribbling system nonetheless. Researchers from MIT&#39;s Improbable Artificial Intelligence Lab, part of the Computer Science and Artificial Intelligence Laboratory (CSAIL), have developed a legged robotic system that can dribble a soccer ball under the same conditions as humans. The bot used a mixture of onboard sensing and computing to traverse different natural terrains such as sand, gravel, mud, and snow, and adapt to their varied impact on the ball&rsquo;s motion. Like every committed athlete, &quot;DribbleBot&quot; could get up and recover the ball after falling.&nbsp;Programming robots to play soccer has been an active research area for some time. However, the team wanted to automatically learn how to actuate the legs during dribbling, to enable the discovery of hard-to-script skills for responding to diverse terrains like snow, gravel, sand, grass, and pavement. Enter, simulation.&nbsp;A robot, ball, and terrain are inside the simulation &mdash; a digital twin of the natural world. You can load in the bot and other assets and set physics parameters, and then it handles the forward simulation of the dynamics from there. Four thousand versions of the robot are simulated in parallel in real time, enabling data collection 4,000 times faster than using just one robot. That&#39;s a lot of data.&nbsp;<iframe width="640" height="360" src="https://www.youtube.com/embed/bV4ek-zI3CU?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>Video: MIT CSAIL&nbsp;The robot starts without knowing how to dribble the ball &mdash; it just receives a reward when it does, or negative reinforcement when it messes up. So, it&#39;s essentially trying to figure out what sequence of forces it should apply with its legs. &quot;One aspect of this reinforcement learning approach is that we must design a good reward to facilitate the robot learning a successful dribbling behavior,&quot; says MIT PhD student Gabe Margolis, who co-led the work along with Yandong Ji, research assistant in the Improbable AI Lab. &quot;Once we&#39;ve designed that reward, then it&#39;s practice time for the robot: In real time, it&#39;s a couple of days, and in the simulator, hundreds of days. Over time it learns to get better and better at manipulating the soccer ball to match the desired velocity.&quot;&nbsp;The bot could also navigate unfamiliar terrains and recover from falls due to a recovery controller the team built into its system. This controller lets the robot get back up after a fall and switch back to its dribbling controller to continue pursuing the ball, helping it handle out-of-distribution disruptions and terrains.&nbsp;&quot;If you look around today, most robots are wheeled. But imagine that there&#39;s a disaster scenario, flooding, or an earthquake, and we want robots to aid humans in the search-and-rescue process. We need the machines to go over terrains that aren&#39;t flat, and wheeled robots can&#39;t traverse those landscapes,&quot; says Pulkit Agrawal, MIT professor, CSAIL principal investigator, and director of Improbable AI Lab.&quot; The whole point of studying legged robots is to go terrains outside the reach of current robotic systems,&quot; he adds. &quot;Our goal in developing algorithms for legged robots is to provide autonomy in challenging and complex terrains that are currently beyond the reach of robotic systems.&quot;&nbsp;The fascination with robot quadrupeds and soccer runs deep &mdash; Canadian professor Alan Mackworth first noted the idea in a paper entitled &quot;On Seeing Robots,&quot; presented at VI-92, 1992. Japanese researchers later organized a workshop on &quot;Grand Challenges in Artificial Intelligence,&quot; which led to discussions about using soccer to promote science and technology. The project was launched as the Robot J-League a year later, and global fervor quickly ensued. Shortly after that, &quot;RoboCup&quot; was born.&nbsp;Compared to walking alone, dribbling a soccer ball imposes more constraints on DribbleBot&#39;s motion and what terrains it can traverse. The robot must adapt its locomotion to apply forces to the ball to &nbsp;dribble. The interaction between the ball and the landscape could be different than the interaction between the robot and the landscape, such as thick grass or pavement. For example, a soccer ball will experience a drag force on grass that is not present on pavement, and an incline will apply an acceleration force, changing the ball&rsquo;s typical path. However, the bot&#39;s ability to traverse different terrains is often less affected by these differences in dynamics &mdash; as long as it doesn&#39;t slip &mdash; so the soccer test can be sensitive to variations in terrain that locomotion alone isn&#39;t.&nbsp;&quot;Past approaches simplify the dribbling problem, making a modeling assumption of flat, hard ground. The motion is also designed to be more static; the robot isn&rsquo;t trying to run and manipulate the ball simultaneously,&quot; says Ji. &quot;That&#39;s where more difficult dynamics enter the control problem. We tackled this by extending recent advances that have enabled better outdoor locomotion into this compound task which combines aspects of locomotion and dexterous manipulation together.&quot;On the hardware side, the robot has a set of sensors that let it perceive the environment, allowing it to feel where it is, &quot;understand&quot; its position, and &quot;see&quot; some of its surroundings. It has a set of actuators that lets it apply forces and move itself and objects. In between the sensors and actuators sits the computer, or &quot;brain,&quot; tasked with converting sensor data into actions, which it will apply through the motors. When the robot is running on snow, it doesn&#39;t see the snow but can feel it through its motor sensors. But soccer is a trickier feat than walking &mdash; so the team leveraged cameras on the robot&#39;s head and body for a new sensory modality of vision, in addition to the new motor skill. And then &mdash; we dribble.&nbsp;&quot;Our robot can go in the wild because it carries all its sensors, cameras, and compute on board. That required some innovations in terms of getting the whole controller to fit onto this onboard compute,&quot; says Margolis. &quot;That&#39;s one area where learning helps because we can run a lightweight neural network and train it to process noisy sensor data observed by the moving robot. This is in stark contrast with most robots today: Typically a robot arm is mounted on a fixed base and sits on a workbench with a giant computer plugged right into it. Neither the computer nor the sensors are in the robotic arm! So, the whole thing is weighty, hard to move around.&quot;There&#39;s still a long way to go in making these robots as agile as their counterparts in nature, and some terrains were challenging for DribbleBot. Currently, the controller is not trained in simulated environments that include slopes or stairs. The robot isn&#39;t perceiving the geometry of the terrain; it&#39;s only estimating its material contact properties, like friction. If there&#39;s a step up, for example, the robot will get stuck &mdash; it won&#39;t be able to lift the ball over the step, an area the team wants to explore in the future. The researchers are also excited to apply lessons learned during development of DribbleBot to other tasks that involve combined locomotion and object manipulation, quickly transporting diverse objects from place to place using the legs or arms.&ldquo;DribbleBot is an impressive demonstration of the feasibility of such a system in a complex problem space that requires dynamic whole-body control,&rdquo; says Vikash Kumar, a research scientist at Facebook AI Research who was not involved in the work. &ldquo;What&rsquo;s impressive about DribbleBot is that all sensorimotor skills are synthesized in real time on a low-cost system using onboard computational resources. While it exhibits remarkable agility and coordination, it&rsquo;s merely &#39;kick-off&rsquo; for the next era. Game-On!&rdquo;The research is supported by the DARPA Machine Common Sense Program, the MIT-IBM Watson AI Lab, the National Science Foundation Institute of Artificial Intelligence and Fundamental Interactions, the U.S. Air Force Research Laboratory, and the U.S. Air Force Artificial Intelligence Accelerator. The paper will be presented at the 2023 IEEE International Conference on Robotics and Automation (ICRA).<hr>&quot;Reprinted with permission of&nbsp;<a href="https://news.mit.edu/2023/legged-robotic-system-playing-soccer-various-terrains-0403" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>&quot; &nbsp;

Researchers created DribbleBot, a system for in-the-wild dribbling on diverse natural terrains including sand, gravel, mud, and snow using onboard sensing and computing. In addition to these football feats, such robots may someday aid humans in search-and-rescue missions. Photo: Mike Grimmett/MIT CSAIL

“DribbleBot” can maneuver a soccer ball on landscapes such as sand, gravel, mud, and snow, using reinforcement learning to adapt to varying ball dynamics.

A four-legged robotic system for playing soccer on various terrains

Coordinated Movement has always been one of the primary challenges to tackle in robotics. Locomotion and manipulation are the two main concepts of movement. Locomotion describes the method a robot uses to transport itself from one place to another. This can take the form of legged, wheeled, or tracked motion. Manipulation, on the other hand, describes the manner in which a robot interacts with objects around it. That includes grabbing an object, picking something up, throwing something, or even opening a door.For years, researchers and engineers have studied ways to control locomotion and manipulation simultaneously. Particularly for legged robots, attaching an arm can enable them to perform several mobile manipulation tasks that wheeled or tracked robots cannot perform. The conventional way of controlling such legged manipulators has been to control locomotion and manipulation separately - otherwise known as decoupling mobility and manipulation. While this method has its perks, it does require a lot of engineering efforts to maintain sufficient coordination between the legs and the arm.&nbsp;&nbsp;<iframe id="638d493a4c420f000872b315" width="250" class="specs-iframe" height="400" src="https://wevolver.com/iframe/specs/unitree-robotics-go1-edu-plus?iframe=true" frameborder="0" scrolling="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>Researchers refer to modeling an arm-carrying legged robot as &ldquo;a dynamic, high-degree-of-freedom, and non-smooth control problem.&rdquo; This means the complexity of such a model would limit its operation to within specified settings. Venturing into different real-life scenarios would make it increasingly complex and burdensome, and errors can arise, leading to undesirable and unnatural movements.This has driven engineers to explore different methods that can bring synergy between the limbs in a way that resembles biologically coordinated movement. Think of the way your arms and legs move and how interconnected they are. For instance, it is difficult to move your left arm and your left leg in different motions while standing. The same applies to your right side. Or consider the activity of picking up something on the ground. Your legs and arm would work together to help you reach the object and pick it up. This is referred to as interlimb neural coupling. In simpler terms, it is called whole-body control. It allows for coordination between the limbs and the extension of the capabilities of individual parts. In a similar sense, researchers at Carnegie Mellon University have successfully developed a unified policy that governs an arm-carrying legged robot in a whole-body control manner using the concept of reinforcement learning (RL). This allowed them to train the robot to synergize its locomotion and manipulation and perform specific tasks through a dynamic and agile behavior.<iframe width="640" height="360" src="https://www.youtube.com/embed/t6NoCx_zitM?autoplay=1&mute=1&wmode=opaque&rel=0" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe><h2 dir="ltr" id="isPasted">A Unified Policy for Coordinated Locomotion and Manipulation</h2>Earlier this year, Zipeng Fu, Xuxin Cheng, and Deepak Pathak from Carnegie Mellon University[1] set out to test whether a learning-based, unified policy would outperform existing decoupling techniques in controlling a robot&rsquo;s movement and ability to interact with its surrounding. As they explained, hierarchy-based decoupled and semi-coupled control methods have been ineffective due to a &ldquo;lack of coordination between the arm and legs, error propagation across modules, and slow, non-smooth and unnatural motions.&rdquo;The unified policy was based on a machine-learning training concept called reinforcement learning (RL). This method simply rewards desirable behaviors and punishes undesirable ones. Through RL, the robot would learn by trial and error to perceive and analyze its environment and act accordingly.The study comprised simulation experiments, which were then transferred into real-world experimentation. However, three distinct issues made it challenging beyond the standard RL model of training in simulation and transferring to the real world:<ol><li dir="ltr">Multiple degrees of freedom (12 DoFs in the quadruped robot and 6 DoF in the attached arm)</li><li dir="ltr">Conflicting objectives in mobility and weight balance</li><li dir="ltr">Dependency between manipulation and locomotion&nbsp;&nbsp;</li></ol>This is why Fu et al. proposed adding&nbsp;regularized online adaptation&nbsp;to bridge the so-called Sim2Real gap - i.e., the information gap between the skills acquired in simulation and those transferred to the real-world system; also referred to as the&nbsp;realizability gap. Regularization is a machine learning technique that helps calibrate the model and minimize the deviation between the model and the dataset, thus minimizing the realizability gap. This adaptation helps eliminate the conventional, two-phase, teacher-student scheme used in previous Sim2Real transfer applications. This is where a &ldquo;teacher network&rdquo; is trained by RL in simulation using full-state information, and a &ldquo;student network&rdquo; attempts to imitate it in real life based on partial information from onboard observations.&nbsp;<iframe id="6389f2641a0dc20008fdb9d3" width="250" class="specs-iframe" height="400" src="https://wevolver.com/iframe/specs/interbotix-widowx-250-6dof?iframe=true" frameborder="0" scrolling="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>The researchers also tackled the dependency issue by proposing the concept of&nbsp;advantage mixing. Advantage functions describe how much a particular action is a good or bad decision given a specific state. Knowing that locomotion tasks mostly rely on leg activity and manipulation tasks on arm activity, mixing the advantage functions of both locomotion and manipulation can help encourage and boost the robot&rsquo;s learning of the unified policy.<h2 dir="ltr">A Low-Cost Legged-Robot with Whole-Body Control</h2>The researchers presented a simple design for the legged manipulator made of the following elements:<ol><li dir="ltr">A 4-legged robot platform</li><li dir="ltr">A robot arm on top of the platform</li><li dir="ltr">An RGB camera positioned next to the gripper of the arm</li><li dir="ltr">An onboard battery to power the arm and the quadruped robot</li></ol><h2 dir="ltr">Robust Unified Policy in Simulation and Real World</h2>With this design, they were able to show in the simulation experiments an increase in whole-body coordination as the legs helped the arm reach beyond its own workspace by bending to reach lower objects (e.g. picking up a cup) and standing up high to reach higher objects (e.g. wiping a whiteboard). Similarly, the arm helped the legs maintain balance even under relatively large disturbances (e.g. running with an initial speed of 1 m/s). This demonstrates a solid unified policy with a high survival rate.Advantage mixing also helped the policy focus on each task and then combine them to induce a mechanism that boosts training. Furthermore, the proposed regularized adaptation resulted in improved performance and better prediction of the environment, as errors at the level of the gripper dropped by 20% compared to other systems, such as the two-phase teacher-student scheme.As for the real-world experiments, the researchers studied three activities: teleoperation using joysticks, closed-loop control through RGB vision-guided tracking, and open-loop response to human demonstrations.<ul><li dir="ltr">For teleoperation, the researchers used two joysticks to command the end-effector (gripper) to reach particular points even beyond the space of training. They found out that both the leg and arm joints coordinated to help the end-effector reach those points.</li><li dir="ltr">During vision-guided tracking, the RGB camera was used alongside the joystick control to provide visual feedback to the robot and improve its picking tasks. It showed repeated success on both easy and hard tasks.</li><li dir="ltr">In the open-loop control experiments, the researchers tested the coupling of the robot&rsquo;s agile locomotion with its dynamic arm movement. They commanded the end-effector to follow a predefined path while the robot was walking. The results showed dynamic coordination even while walking on an uneven grass area.</li></ul><iframe width="640" height="360" src="https://www.youtube.com/embed/zhpI3JTqQGI?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> <h2 dir="ltr">How the Unified Policy Approach be Further Expanded</h2>The success of the unified policy opens up a new scope of coordinated robotic movement. As the researchers suggested, those results are merely preliminary. Further exciting research is expected to stem from this work, such as enabling the robot to climb on a table using its front legs to pick something up from atop the table. Another suggestion was to mount a camera onto the center of the torso, providing different visual feedback, which would pave the road for vision-based policy learning.<a href="https://manipulation-locomotion.github.io/" target="_blank">Read the full study and learn more about the exciting research of Fu et al. by visiting their dedicated website.</a>1.&nbsp;Zipeng , F., Cheng, X. and Pathak, D. (2022) &ldquo;Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion ,&rdquo;&nbsp;CoRL 2022 Conference Paper&nbsp;[Preprint].&nbsp;

Could this be the time we say goodbye to robotic awkwardness and hello to seamless motion? This article highlights the groundbreaking concept of whole-body control, a coordinated robotic motion technique that resembles natural movement. Imagine a robot smoothly navigating its environment, performing tasks with grace and precision. This innovative approach allows for improved efficiency and greater versatility in real-world settings.


Unifying Robotic Arm and Leg Movement for Whole-Body Control

DEEP Robotics recently released its newest robotics technique, of autonomous coordinated multi-robot exploration; the experiment occurred in a 3,000 m2&nbsp;unknown environment with five Jueying X20 robot dogs. The experiment accomplished the coordinated exploration of eight targets along with its fully autonomous decision-making and planning. The purpose of the experiment is to seek out the infinite applicable potentials in the field of rescue and exploration. &nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjY3NDY4NDc4NTMzLTE2Njc0Njg0Nzg1MzMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">Multiple Jueying robot dogs exploring under unknown environments togetherDuring rescue and exploration, collecting information is often found insufficient in manned operations; operators often encounter difficult situations such as anoxia, toxic gas, landslip, etc. The quadruped robot can assist or replace operators to get into the complex and hazardous areas that wheel or tracked robots have difficulties accessing. The technique of coordinated quadruped robot exploration explored information and paths shared between robots, which enhanced exploration efficiency under unknown environments.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjY3NDY4NTU0MTQ0LTE2Njc0Njg1NTQxNDQucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">Jueying X20 discovered and recognized the targetThe technique of autonomous exploration under an unknown environment is a frontier in the research of robotics, in which specific movement coordination, like robot dance, is unable to compare with its quality and difficulty. Jueying robots were equipped with a high-resolution omnidirectional camera, Lidar, and mesh network during the experiment. The robots build the map together by Lidar and locating algorithm; they use the omnidirectional camera for deep learning and target recognition. Each robot dog autonomously assigns its unexplored area in real-time by the cooperative mechanism. In the progress of exploration, robots autonomously planned workable paths for obstacle avoidance; eventually pulled off coordinated robot exploration and target recognition in a largely unknown area.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjY3NDY4NTc0NDMzLTE2Njc0Njg1NzQ0MzMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">Jueying X20 robots share information through tracked signalsTang Wei, the manager of the Perception algorithm department, introduced: The key to an efficient, coordinated exploration is the cooperative algorithm. The robots share the simplest and most useful information, which can gradually reduce bandwidth communication cost and enhance the system&#39;s adaptability under Bandwidth-constrained Conditions. The research was issued 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR2022).<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjY3NDY4NjAwODA0LTE2Njc0Njg2MDA4MDQucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">5 Juying X20 robot dogs share the boundary of explored area with each otherDEEP Robotics is a leading quadruped robot company in Asia; the self-developed &quot;Jueying&quot; series robots are already applied in power inspection, public rescue, discovery, and other applicable fields with its advanced locomotion control algorithm, environmental perception, and AI algorithm. Besides the coordinated multi-robot exploration, DEEP Robotics has tried corporative operation with other forms of robots from the Huzhou institute of Zhejiang University in the &quot;yellow dragon&quot; cave.

DEEP Robotics recently released its newest robotics technique, which is autonomous coordinated multi-robot exploration; the experiment is operated under a 3,000 m2 unknown environment with five Jueying X20 robot dogs.

Coordinated multi-robot dog exploration in unknown environment

It would be awesome to have robots that can help us do important things, like aid disaster recovery efforts or monitor the environment. In the case of quadrupeds, robots that walk on four legs, their mobility requires many software components to work together seamlessly. It&rsquo;s a drag when all your time is spent on developing lower-level infrastructure instead of focusing on high-level behaviors. <a href="https://engineering.cmu.edu/directory/bios/johnson-aaron.html" id="isPasted" target="_blank">Aaron Johnson</a>&rsquo;s <a href="https://www.cmu.edu/me/robomechanicslab/" rel="noopener" target="_blank">Robomechanics Lab</a> experienced these frustrations firsthand. His team often had to rely on simple models for doing research because existing software solutions were not open sourced, did not provide a modular framework, and lacked end-to-end functionality.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjU5NzE4NTkzNzMxLTA3MjYtZW0tMS1xdWFkcnVwZWQucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">A four-legged robot using Quad-SDK software is shown jumping. Source:&nbsp;Carnegie Mellon University/Robomechanics Lab&nbsp;An example of innovation born out of necessity, Johnson, an associate professor of <a href="https://www.meche.engineering.cmu.edu/index.html" id="isPasted" rel="noopener" target="_blank">mechanical engineering</a>, and his team designed their own locomotion software stack, <a href="https://github.com/robomechanics/quad-sdk" rel="noopener" target="_blank">Quad-SDK</a>, a full-stack framework for agile quadrupedal locomotion. Many graduate students have contributed to the development of the software across a two-year period, but it was time well spent. Their design can simplify the development process for roboticists everywhere because it uses an open-source license, meaning the software can be used and modified as the user wishes. Quad-SDK comes ready to use, so researchers don&rsquo;t have to worry about implementing the necessary tools and infrastructure; instead, they can get right to work on exciting behaviors and applications.&nbsp;<iframe src="https://www.youtube.com/embed/kSXKjTxKpuA?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" style="width: 767px; height: 430px;"></iframe>Unlike other options, Quad-SDK is also compatible with the Robot Operating System (ROS). ROS is a middleware, somewhere between hardware and software, that lets different parts of a system talk to each other. For example, if a robot senses an obstacle in its path and needs to transfer information from its perception module to its decision-making module, ROS is what enables that communication. Imagine an app that doesn&rsquo;t use iOS or Android&mdash;it&rsquo;s much simpler when everything works together.Locomotion is a layered problem, as team member and Ph.D. student Ardalan Tajbakhsh describes it. &ldquo;In order to do anything meaningful on a robot, you need to have many components working together seamlessly.&rdquo; Quad-SDK provides a framework for robotics researchers and developers to focus their efforts on the core algorithms instead of the software tooling and infrastructure.<blockquote>In order to do anything meaningful on a robot, you need to have many components working together seamlessly. - Ardalan Tajbakhsh, Ph.D. student, Mechanical Engineering&nbsp;</blockquote>Other software packages are very good at solving one component, like motion planning, but it&rsquo;s critical to have end-to-end frameworks that provide the necessary algorithms, tools, and infrastructure for performing high-quality robotics research. Quad-SDK is full stack, meaning it contains every level of the hierarchy that affects quadruped locomotion, starting with global planning. This layer sits at the top of the stack; Tajbakhsh compares it to Google Maps, because it chooses where the robot should roughly go to reach its destination. The next layer, local planner, is what decides the specifics of the route, like where the robot should place its feet. This culminates in a third layer, the robot driver, which sends commands to the quadruped&rsquo;s joints to execute the desired motion.In the weeks since Quad-SDK <a href="https://leggedrobots.org/contributions.html" rel="noopener" target="_blank">won a Best Paper Award</a> at the Workshop on Legged Robots at the 2022 IEEE International Conference on Robotics and Automation (ICRA), people have already requested to use it, a clear indicator of how useful its solutions are and how the open-source format invites collaboration within the robotics community. Figuratively and literally, Quad-SDK can go many places.

Aaron Johnson’s Robomechanics Lab has developed an open-source software that enables more agile movement in legged robots.

Open-source software gives a leg up to robot research

A praying mantis for the moon, Mars and meteorites: the mantis-like robot "Mantis" is said to be able to independently explore planets and build an infrastructure with its claws. The hardware and software are developed by scientists from the Robotics Innovation Center at the German Research Center for Artificial Intelligence (DFKI) and the University of Bremen.&nbsp;Thanks to his bio-inspired body and various running patterns, he will master steep craters and boulder fields as well as manipulate with his front legs.&nbsp;By learning from his experiences, he should be able to act purposefully.&nbsp;The LIMES project that has just started is being funded with around 3.7 million euros by the German Aerospace Center (DLR).&nbsp;The term is four years.&nbsp;LIMES stands for “Learning Intelligent Motions for Kinematically Complex Legged Robots for Exploration in Space”. The name of the robot is borrowed from the mantis, which belongs to the mantis order.&nbsp;The robot morphology will be similar to that of the natural model: the system should not only be able to use the two front of its six legs to walk, but also to grab objects.&nbsp;To do this, it lifts its upper body – like the praying mantis. "The challenge is to learn running patterns for different ground conditions in which all six legs work in unison despite different tasks," explains Prof. Dr.&nbsp;Frank Kirchner, head of the DFKI Robotics Innovation Center and the Robotics working group at the University of Bremen.&nbsp;The many joints, distributed over several legs, enable many different movement patterns.&nbsp;In this way, the robot can adapt its posture to the circumstances – and maneuver safely in unfamiliar terrain. Robot selects situationally from its "behaviour library" The aim of the project is to give the robot algorithms for different walking modes.&nbsp;These can be designed for energy-saving or careful movement.&nbsp;Mantis senses stumbling blocks, craters and hills with tactile sensors that he wears in his soles.&nbsp;Using a camera in his head, he creates a 3D image of the environment.&nbsp;The data is transmitted to earth by satellite.&nbsp;The human instructs the robot which destination to head for.&nbsp;"Based on the data, Mantis selects the appropriate walking pattern from its so-called behavior library.&nbsp;It recognizes obstacles and then avoids them of its own accord,” says Sebastian Bartsch, DFKI computer scientist and project manager.&nbsp;After each action, the robot evaluates its decision.&nbsp;“For example, he asks himself: Was my energy use good?&nbsp;If not, he will act differently in a similar situation in the future,” says Bartsch. The movement patterns are pre-learned in simulations that simulate extraterrestrial conditions: gravitation, lighting conditions, gradients. Real tests follow in the DFKI's space exploration hall, a 288 square meter test site with an artificial crater landscape.&nbsp;

Inspired by the mantis: Robot "Mantis" will not only be able to walk with its front legs, but also to manipulate.

A praying mantis for the moon, Mars and meteorites: the mantis-like robot "Mantis" is said to be able to independently explore planets and build an infrastructure with its claws. 

The Praying Mantis As A Model - Six-Legged Robot To Build Infrastructure On Celestial Bodies

Steep sections on slippery ground, high steps, scree and forest trails full of roots: the path up the 1,098-metre-high Mount Etzel at the southern end of Lake Zurich is peppered with numerous obstacles. But ANYmal, the quadrupedal robot from the Robotic Systems Lab at ETH Zurich, overcomes the 120 vertical metres effortlessly in a 31-minute hike. That’s 4&nbsp;minutes faster than the estimated duration for human hikers – and with no falls or missteps.This is made possible by a new control technology, which researchers at ETH Zurich led by robotics professor Marco Hutter recently presented in the journal <a target="_blank">Science Robotics</a>. “The robot has learned to combine visual perception of its environment with proprioception – its sense of touch – based on direct leg contact. This allows it to tackle rough terrain faster, more efficiently and, above all, more robustly,” Hutter says. In the future, ANYmal can be used anywhere that is too dangerous for humans or too impassable for other robots.<iframe src="https://www.youtube.com/embed/0e40Umfnl_w?&amp;wmode=opaque&amp;rel=0" frameborder="0" allowfullscreen="" class="fr-draggable" style="width: 767px; height: 431px;"></iframe>Video: Nicole Davidson / ETH Zurich&nbsp;<h2 id="isPasted">Perceiving the environment accurately</h2>To navigate difficult terrain, humans and animals quite automatically combine the visual perception of their environment with the proprioception of their legs and hands. This allows them to easily handle slippery or soft ground and move around with confidence, even when visibility is low. Until now, legged robots have been able to do this only to a limited extent.“The reason is that the information about the immediate environment recorded by laser sensors and cameras is often incomplete and ambiguous,” explains Takahiro Miki, a doctoral student in Hutter’s group and lead author of the study. For example, tall grass, shallow puddles or snow appear as insurmountable obstacles or are partially invisible, even though the robot could actually traverse them. In addition, the robot’s view can be obscured in the field by difficult lighting conditions, dust or fog.“That’s why robots like ANYmal have to be able to decide for themselves when to trust the visual perception of their environment and move forward briskly, and when it is better to proceed cautiously and with small steps,” Miki says. “And that’s the big challenge.”<h2>A virtual training camp</h2>Thanks to a new controller based on a neural network, the legged robot ANYmal, which was developed by ETH Zurich researchers and commercialized by the ETH spin-off <a href="https://www.anybotics.com/company/" target="_blank">ANYboticscall_made</a>, is now able to combine external and proprioceptive perception for the first time. Before the robot could put its capabilities to the test in the real world, the scientists exposed the system to numerous obstacles and sources of error in a virtual training camp. This let the network learn the ideal way for the robot to overcome obstacles, as well as when it can rely on environmental data – and when it would do better to ignore that data.“With this training, the robot is able to master the most difficult natural terrain without having seen it before,” says ETH Zurich Professor Hutter. This works even if the sensor data on the immediate environment is ambiguous or vague. ANYmal then plays it safe and relies on its proprioception. According to Hutter, this allows the robot to combine the best of both worlds: the speed and efficiency of external sensing and the safety of proprioceptive sensing.<h2>Use under extreme conditions</h2>Whether after an earthquake, after a nuclear disaster, or during a forest fire, robots like ANYmal can be used primarily wherever it is too dangerous for humans and where other robots cannot cope with the difficult terrain.In September of last year, ANYmal was able to demonstrate just how well the new control technology works at the <a href="https://mavt.ethz.ch/de/news-und-veranstaltungen/d-mavt-news/2021/09/siegreiche-teilnahme-an-robotik-wettbewerb.html" target="_blank">DARPA Subterranean Challenge</a>, the world’s best-known robotics competition. The ETH Zurich robot automatically and quickly overcame numerous obstacles and difficult terrain while autonomously exploring an underground system of narrow tunnels, caves, and urban infrastructure. This was a major part of why the ETH Zurich researchers, as part of the CERBERUS team, took first place with a prize of 2 million dollars.<hr><h2>Reference</h2>Miki T, Lee J, Hwangbo J, Wellhausen L: Learning robust perceptive locomotion for quadrupedal robots in the wild, Science Robotics 2022, doi: <a href="https://doi.org/10.1126/scirobotics.abk2822" target="_blank">10.1126/scirobotics.abk2822call_made</a>

The legged robot ANYmal on the rocky path to the summit of Mount Etzel, which stands 1,098 metres above sea level. (Photo: Takahiro Miki)

ETH researchers led by Marco Hutter developed a new control approach that enables a legged robot, called ANYmal, to move quickly and robustly over difficult terrain. Thanks to machine learning, the robot can combine its visual perception of the environment with its sense of touch for the first time.

How robots learn to hike

The Spacebit Engineering Challenge enables engineers around the world to contribute to the development of “Asagumo 2.0”, a four-legged walking rover that is set to fly to the Moon with the NASA Commercial Lunar Payload Services (CLPS) program in 2021 and 2022.Spacebit is a UK-based space startup developing space robotic technology and space data analytics tools for space exploration that includes advanced micro robotics and Artificial Intelligence (AI). Their approach to engineering is to deliver competitive cost solutions for the exploration of celestial bodies.Spacebit's goal is to create new opportunities for industry, academia, and government, advancing the future development of a new infrastructure and commercial resource exploration ‘offworld’ - &nbsp;beginning with the Moon.The company's micro-rover technologies and rover deployment systems are designed to carry scientific payloads in support of the Artemis Program space exploration and research goals. Spacebit is the only commercial company to secure two consecutive NASA Commercial Lunar Payload Services (CLPS) delivery missions to the Moon in 2021. Spacebit is one of the first commercial lunar companies that plans to fly on every NASA CLPS delivery on the market.Future versions of the rover would be sent to explore tubular cave structures – subsurface tunnels, believed to have been formed by ancient basaltic lava flows – to see if they are viable locations in which to build future lunar habitats for human presence on the moon.<iframe src="https://www.youtube.com/embed/QSGFL4W1guk?&amp;feature=emb_title&amp;ab_channel=SpacebitCompany&amp;wmode=opaque&amp;rel=0" frameborder="0" allowfullscreen="" class="fr-draggable" style="width: 788px; height: 406px;"></iframe><h2 dir="ltr">Contribute and get your name on the Moon</h2>Over the next several weeks Spacebit will invite the Wevolver community in an open format, for inputs and ideas on various engineering challenges as they develop new rover designs and technologies for space-based applications, beginning with the Moon. The technical feedback will be used to help improve and iterate Spacebit’s technology.The engineering inputs will be assessed and the most valuable contributors will be rewarded by having their name engraved on the Asagumo rover before it launches on its maiden journey to the Moon. Additionally, the winning engineer will be invited to the control room in the UK or US; or, participate in Spacebit’s remote Robotic Operations &amp; Control Center (ROCC). <div class="fr-img-space-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjIzMzMzNTM3MDkwLVNwYWNlYml0LTZiLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 788px;" class="fr-fic fr-dib">The winner of the challenge will have their name engraved on the rover and be invited to the launch control room. Image credit: Spacebit/Wevolver.<h2 class="fr-img-space-wrap2">Asagumo</h2></div>Asagumo is a semi-autonomous micro rover built on a single-unit CubeSat frame, usually used for micro &nbsp;satellites. The &nbsp;rover weighs just 1.3 kilograms, is solar-powered and instead of wheels or tracks, is equipped with four spider-like legs. Mission 1 is a tech demo mission where Asagumo is equipped with sensors and camera technology; the rover will attempt to walk for ≤10 meters on the lunar surface, sending data back to Earth. The goal is to eventually explore the moon’s subsurface through the labyrinth of lunar lava tubes and caves. Because of its four legs rather than wheels or tracks, the rover has an advantageous design to explore parts of the Moon other rovers &nbsp;cannot reach.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjIzMjUzNTcyMTE4LVNwYWNlYml0LTEuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 788px;" class="fr-fic fr-dib" alt="A small square gold rover robot with four black spider-like legs.">The Asagumo micro rover is built on a single-unit CubeSat frame. Image credit: Spacebit.&nbsp;<h2 class="fr-img-space-wrap2">How to contribute?</h2>Joining the discussion is simple. On Wevolver's social media channels, we will share regular updates regarding the progress of the project. Spacebit will ask for feedback on engineering challenges they have on which you can comment and engage with. To make sure you stay in the loop, follow their company profile on <a href="https://www.wevolver.com/profile/spacebit" target="_blank">Wevolver</a>.We're excited to see your feedback, and look forward to seeing the first Wevolver community member inscribed on the Moon!

Contribute to the development of a four-legged walking rover that is set to fly to the Moon with NASA in 2021 and 2022.

The Spacebit Engineering Challenge

Test publishing

Test 20200925

<div data-wow-delay=".09s">A frenetic, hopping rodent is giving University of Michigan engineers new ways to think about and design two-legged robots.Biologists and engineers from U-M, Harvard University and the University of California, San Diego used information theory to, for the first time, quantify the entropy in an animal’s gait in three dimensions. The team’s findings, published in Nature Communications on Sept. 5, could help engineers make biomimetic robots that are tailored to specific environments.“This work points at a new way of building a robot that may be useful in navigating desert environments or desert planets,” said <a href="https://me.engin.umich.edu/people/faculty/ramanarayan-vasudevan" rel="noopener noreferrer" target="_blank">Ram Vasudevan</a>, U-M assistant professor of mechanical engineering and a co-author of the paper. “We want to try and build robots that are capable of thriving in all sorts of environments.”He recalled a time when NASA’s wheeled Curiosity rover got stuck in the Martian desert.“It dug a deeper and deeper hole,” Vasudevan said. “What if they had taken a look at how these rodents travel through the desert? Evolution has found a suitable solution several times over. Until now, we haven’t appreciated how useful a system it is in this context.”<iframe src="https://www.youtube.com/embed/35MSBu7-mk0?&amp;wmode=opaque" allowfullscreen="" class="fr-draggable" style="width: 800px; height: 421px;" frameborder="0"></iframe>The researchers explain their approach to studying the jerboa and its future applications to robotics. Video: Evan Dougherty, Michigan Engineering Communications &amp; Marketing.<div data-wow-delay=".09s">For biologists seeking to understand the ecological structure of animal communities, the findings also show how sudden changes in direction, gait and speed help the jerboa—a bipedal desert rodent—elude hungry predators and gain a competitive edge over their quadrupedal neighbors. The researchers compared the behavior of jerboas to jirds, which have four legs and are in the gerbil family.<h4>Entropy is the answer</h4>The jerboa is native to North Africa and Central Asia. Although bipedalism has evolved multiple times in rodents—including kangaroo rats and Australian hopping mice—jerboas are the only group to use three different gaits when moving on their long hind legs: hopping, skipping and running. They can leap more than half a meter high, which is about ten times their hip height.Talia Moore, a postdoctoral research fellow in the U-M Department of Ecology and Evolutionary Biology, was tasked with characterizing the jerboa’s locomotion and comparing it to other bipedal animals.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1563294792374-jerboa-gif-5.gif" style="width: 766px;" class="fr-fic fr-dib">IMAGE: &nbsp;In order to study a jerboa’s gait, the researchers had the rodent run laps around a track with a strategically placed high-speed camera and force plate. Credit: Talia Moore&nbsp;“When we put the jerboa on a force platform, every step had a different signature,” Moore said. “I thought, ‘This is so weird. It’s like they’re completely unpredictable and I’m unable to characterize it based on the techniques we have.’”She consulted with Vasudevan. “I said, ‘Ram, I wish there was a way to measure unpredictability,’” Moore recalls. “He said, ‘There is.’”They borrowed a metric of randomness from information theory—entropy. Entropy is a measure of the uncertainty of an outcome. With it, they developed a model that calculates the probability that the animal will move in a specific way.Moore and her colleagues suspected that these herky-jerky movements help jerboas foil an aerial predator’s efforts to calculate an intercept course.<h4>Jerboas vs. jirds in the wild and the lab</h4>Since they wanted to see how these animals behave in their natural habitat, they traveled to northwestern China and designed a novel set of field experiments to study whether jirds and jerboas escape predators in different ways.Combining their observations and their new model, they found that the jerboas were much more unpredictable than the jirds. Their maneuvers included momentary bursts of acceleration, kangaroo-like hopping, erratic zigzagging and acrobatic leaps.To test whether the jerboa’s unpredictability truly corresponds to increased predator-evasion success, the researchers performed behavioral experiments that measured the animals’ tendency to seek shelter, which is called thigmotaxis.Small foraging animals face a conflict between their needs to venture out for food and to stay under cover—to avoid becoming food. Due to this conflict, the amount of time an animal spends in an open area varies with its ability to evade predators. As evasiveness increases, the behavioral affinity to shelter, and so-called “open-field anxiety,” decrease.During simulated predation trials, Moore and her colleagues observed that the bipedal jerboas explored the entire experimental enclosure, while jirds tended to remain near the walled periphery. She performed a second experiment in the lab with captive-bred animals that had never seen predators before. The results from both experiments suggest that bipedalism in rodents is associated with lower thigmotaxis and greater predator evasion in open settings.<h4>Why this matters for robotics</h4>Much of the research into robot locomotion involves smooth movement in low-variability environments. But these findings demonstrate some of the limits to that perspective.For biomimetic robots to interact with the world, we’ll need to engineer unpredictability in motion.<cite>Talia Moore</cite><div data-wow-delay=".09s">“The key insight here is it’s a big engineering challenge to engineer unpredictability this has shown that it’s a worthwhile pursuit,” Vasudevan said.Added Moore, “If we want to make more biomimetic robots that interact with the world around us, we’re going to need to think about how to engineer unpredictability in motion.”The paper is titled “Unpredictability of escape trajectory explains predator 2 evasion ability and microhabitat preference of desert rodents.” Co-authors in addition to Moore and Vasudevan are Kimberly L. Cooper of the University of California, San Diego, and Andrew A. Biewener of Harvard University. The study was funded in part by a Chapman Memorial Scholarship to Moore.</div> </div></div>

IMAGE:  The jerboa - a bipedal desert rodent native to North Africa and Central Asia. Credit: Christian Baker