Tag Archives: Simulation
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!):
DARPA SubT Tunnel Circuit – August 15-22, 2019 – Pittsburgh, Pa., USA
CLAWAR 2019 – August 26-28, 2019 – Kuala Lumpur, Malaysia
IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.
Lakshmi Nair from Georgia Tech describes some fascinating research towards robots that can create their own tools, as presented at ICRA this year:
Using a novel capability to reason about shape, function, and attachment of unrelated parts, researchers have for the first time successfully trained an intelligent agent to create basic tools by combining objects.
The breakthrough comes from Georgia Tech’s Robot Autonomy and Interactive Learning (RAIL) research lab and is a significant step toward enabling intelligent agents to devise more advanced tools that could prove useful in hazardous – and potentially life-threatening – environments.
[ Lakshmi Nair ]
Victor Barasuol, from the Dynamic Legged Systems Lab at IIT, wrote in to share some new research on their HyQ quadruped that enables sensorless shin collision detection. This helps the robot navigate unstructured environments, and also mitigates all those painful shin strikes, because ouch.
This will be presented later this month at the International Conference on Climbing and Walking Robots (CLAWAR) in Kuala Lumpur, Malaysia.
[ IIT ]
You used to have a tail, you know—as an embryo, about a month in to your development. All mammals used to have tails, and now we just have useless tailbones, which don’t help us with balancing even a little bit. BRING BACK THE TAIL!
The tail, created by Junichi Nabeshima, Kouta Minamizawa, and MHD Yamen Saraiji from Keio University’s Graduate School of Media Design, was presented at SIGGRAPH 2019 Emerging Technologies.
[ Paper ] via [ Gizmodo ]
The noises in this video are fantastic.
[ ESA ]
Apparently the industrial revolution wasn’t a thorough enough beatdown of human knitting, because the robots are at it again.
[ MIT CSAIL ]
Skydio’s drones just keep getting more and more impressive. Now if only they’d make one that I can afford…
[ Skydio ]
The only thing more fun than watching robots is watching people react to robots.
[ SEER ]
There aren’t any robots in this video, but it’s robotics-related research, and very soothing to watch.
[ Stanford ]
In case it wasn’t clear, which it wasn’t, this is a Roboy project. And if you didn’t understand that first video, you definitely won’t understand this second one:
Whatever that t-shirt is at the end (Roboy in sunglasses puking rainbows…?) I need one.
[ Roboy ]
By adding electronics and computation technology to a simple cane that has been around since ancient times, a team of researchers at Columbia Engineering have transformed it into a 21st century robotic device that can provide light-touch assistance in walking to the aged and others with impaired mobility.
The light-touch robotic cane, called CANINE, acts as a cane-like mobile assistant. The device improves the individual’s proprioception, or self-awareness in space, during walking, which in turn improves stability and balance.
[ ROAR Lab ]
During the second field experiment for DARPA’s OFFensive Swarm-Enabled Tactics (OFFSET) program, which took place at Fort Benning, Georgia, teams of autonomous air and ground robots tested tactics on a mission to isolate an urban objective. Similar to the way a firefighting crew establishes a boundary around a burning building, they first identified locations of interest and then created a perimeter around the focal point.
[ DARPA ]
I think there’s a bit of new footage here of Ghost Robotics’ Vision 60 quadruped walking around without sensors on unstructured terrain.
[ Ghost Robotics ]
If you’re as tired of passenger drone hype as I am, there’s absolutely no need to watch this video of NEC’s latest hover test.
[ AP ]
As researchers teach robots to perform more and more complex tasks, the need for realistic simulation environments is growing. Existing techniques for closing the reality gap by approximating real-world physics often require extensive real world data and/or thousands of simulation samples. This paper presents TuneNet, a new machine learning-based method to directly tune the parameters of one model to match another using an iterative residual tuning technique. TuneNet estimates the parameter difference between two models using a single observation from the target and minimal simulation, allowing rapid, accurate and sample-efficient parameter estimation.
The system can be trained via supervised learning over an auto-generated simulated dataset. We show that TuneNet can perform system identification, even when the true parameter values lie well outside the distribution seen during training, and demonstrate that simulators tuned with TuneNet outperform existing techniques for predicting rigid body motion. Finally, we show that our method can estimate real-world parameter values, allowing a robot to perform sim-to-real task transfer on a dynamic manipulation task unseen during training. We are also making a baseline implementation of our code available online.
[ Paper ]
Here’s an update on what GITAI has been up to with their telepresence astronaut-replacement robot.
[ GITAI ]
Curiosity captured this 360-degree panorama of a location on Mars called “Teal Ridge” on June 18, 2019. This location is part of a larger region the rover has been exploring called the “clay-bearing unit” on the side of Mount Sharp, which is inside Gale Crater. The scene is presented with a color adjustment that approximates white balancing to resemble how the rocks and sand would appear under daytime lighting conditions on Earth.
[ MSL ]
Some updates (in English) on ROS from ROSCon France. The first is a keynote from Brian Gerkey:
And this second video is from Omri Ben-Bassat, about how to keep your Anki Vector alive using ROS:
All of the ROSCon FR talks are available on Vimeo.
[ ROSCon FR ] Continue reading
Quadrupedal robots are making significant advances lately, and just in the past few months we’ve seen Boston Dynamics’ Spot hauling a truck, IIT’s HyQReal pulling a plane, MIT’s MiniCheetah doing backflips, Unitree Robotics’ Laikago towing a van, and Ghost Robotics’ Vision 60 exploring a mine. Robot makers are betting that their four-legged machines will prove useful in a variety of applications in construction, security, delivery, and even at home.
ANYbotics has been working on such applications for years, testing out their ANYmal robot in places where humans typically don’t want to go (like offshore platforms) as well as places where humans really don’t want to go (like sewers), and they have a better idea than most companies what can make quadruped robots successful.
This week, ANYbotics is announcing a completely new quadruped platform, ANYmal C, a major upgrade from the really quite research-y ANYmal B. The new quadruped has been optimized for ruggedness and reliability in industrial environments, with a streamlined body painted a color that lets you know it means business.
ANYmal C’s physical specs are pretty impressive for a production quadruped. It can move at 1 meter per second, manage 20-degree slopes and 45-degree stairs, cross 25-centimeter gaps, and squeeze through passages just 60 centimeters wide. It’s packed with cameras and 3D sensors, including a lidar for 3D mapping and simultaneous localization and mapping (SLAM). All these sensors (along with the vast volume of gait research that’s been done with ANYmal) make this one of the most reliably autonomous quadrupeds out there, with real-time motion planning and obstacle avoidance.
ANYmal can autonomously attach itself to a cone-shaped docking station to recharge.
ANYmal C is also one of the ruggedest legged robots in existence. The 50-kilogram robot is IP67 rated, meaning that it’s completely impervious to dust and can withstand being submerged in a meter of water for an hour. If it’s submerged for longer than that, you’re absolutely doing something wrong. The robot will run for over 2 hours on battery power, and if that’s not enough endurance, don’t worry, because ANYmal can autonomously impale itself on a weird cone-shaped docking station to recharge.
ANYmal C’s sensor payload includes cameras and a lidar for 3D mapping and SLAM.
As far as what ANYmal C is designed to actually do, it’s mostly remote inspection tasks where you need to move around through a relatively complex environment, but where for whatever reason you’d be better off not sending a human. ANYmal C has a sensor payload that gives it lots of visual options, like thermal imaging, and with the ability to handle a 10-kilogram payload, the robot can be adapted to many different environments.
Over the next few months, we’re hoping to see more examples of ANYmal C being deployed to do useful stuff in real-world environments, but for now, we do have a bit more detail from ANYbotics CTO Christian Gehring.
IEEE Spectrum: Can you tell us about the development process for ANYmal C?
Christian Gehring: We tested the previous generation of ANYmal (B) in a broad range of environments over the last few years and gained a lot of insights. Based on our learnings, it became clear that we would have to re-design the robot to meet the requirements of industrial customers in terms of safety, quality, reliability, and lifetime. There were different prototype stages both for the new drives and for single robot assemblies. Apart from electrical tests, we thoroughly tested the thermal control and ingress protection of various subsystems like the depth cameras and actuators.
What can ANYmal C do that the previous version of ANYmal can’t?
ANYmal C was redesigned with a focus on performance increase regarding actuation (new drives), computational power (new hexacore Intel i7 PCs), locomotion and navigation skills, and autonomy (new depth cameras). The new robot additionally features a docking system for autonomous recharging and an inspection payload as an option. The design of ANYmal C is far more integrated than its predecessor, which increases both performance and reliability.
How much of ANYmal C’s development and design was driven by your experience with commercial or industry customers?
Tests (such as the offshore installation with TenneT) and discussions with industry customers were important to get the necessary design input in terms of performance, safety, quality, reliability, and lifetime. Most customers ask for very similar inspection tasks that can be performed with our standard inspection payload and the required software packages. Some are looking for a robot that can also solve some simple manipulation tasks like pushing a button. Overall, most use cases customers have in mind are realistic and achievable, but some are really tough for the robot, like climbing 50° stairs in hot environments of 50°C.
Can you describe how much autonomy you expect ANYmal C to have in industrial or commercial operations?
ANYmal C is primarily developed to perform autonomous routine inspections in industrial environments. This autonomy especially adds value for operations that are difficult to access, as human operation is extremely costly. The robot can naturally also be operated via a remote control and we are working on long-distance remote operation as well.
Do you expect that researchers will be interested in ANYmal C? What research applications could it be useful for?
ANYmal C has been designed to also address the needs of the research community. The robot comes with two powerful hexacore Intel i7 computers and can additionally be equipped with an NVIDIA Jetson Xavier graphics card for learning-based applications. Payload interfaces enable users to easily install and test new sensors. By joining our established ANYmal Research community, researchers get access to simulation tools and software APIs, which boosts their research in various areas like control, machine learning, and navigation.
[ ANYmal C ] Continue reading
Robots have been masters of manufacturing at speed and precision for decades, but give them a seemingly simple task like stacking shelves, and they quickly get stuck. That’s changing, though, as engineers build systems that can take on the deceptively tricky tasks most humans can do with their eyes closed.
Boston Dynamics is famous for dramatic reveals of robots performing mind-blowing feats that also leave you scratching your head as to what the market is—think the bipedal Atlas doing backflips or Spot the galloping robot dog.
Last week, the company released a video of a robot called Handle that looks like an ostrich on wheels carrying out the seemingly mundane task of stacking boxes in a warehouse.
It might seem like a step backward, but this is exactly the kind of practical task robots have long struggled with. While the speed and precision of industrial robots has seen them take over many functions in modern factories, they’re generally limited to highly prescribed tasks carried out in meticulously-controlled environments.
That’s because despite their mechanical sophistication, most are still surprisingly dumb. They can carry out precision welding on a car or rapidly assemble electronics, but only by rigidly following a prescribed set of motions. Moving cardboard boxes around a warehouse might seem simple to a human, but it actually involves a variety of tasks machines still find pretty difficult—perceiving your surroundings, navigating, and interacting with objects in a dynamic environment.
But the release of this video suggests Boston Dynamics thinks these kinds of applications are close to prime time. Last week the company doubled down by announcing the acquisition of start-up Kinema Systems, which builds computer vision systems for robots working in warehouses.
It’s not the only company making strides in this area. On the same day the video went live, Google unveiled a robot arm called TossingBot that can pick random objects from a box and quickly toss them into another container beyond its reach, which could prove very useful for sorting items in a warehouse. The machine can train on new objects in just an hour or two, and can pick and toss up to 500 items an hour with better accuracy than any of the humans who tried the task.
And an apple-picking robot built by Abundant Robotics is currently on New Zealand farms navigating between rows of apple trees using LIDAR and computer vision to single out ripe apples before using a vacuum tube to suck them off the tree.
In most cases, advances in machine learning and computer vision brought about by the recent AI boom are the keys to these rapidly improving capabilities. Robots have historically had to be painstakingly programmed by humans to solve each new task, but deep learning is making it possible for them to quickly train themselves on a variety of perception, navigation, and dexterity tasks.
It’s not been simple, though, and the application of deep learning in robotics has lagged behind other areas. A major limitation is that the process typically requires huge amounts of training data. That’s fine when you’re dealing with image classification, but when that data needs to be generated by real-world robots it can make the approach impractical. Simulations offer the possibility to run this training faster than real time, but it’s proved difficult to translate policies learned in virtual environments into the real world.
Recent years have seen significant progress on these fronts, though, and the increasing integration of modern machine learning with robotics. In October, OpenAI imbued a robotic hand with human-level dexterity by training an algorithm in a simulation using reinforcement learning before transferring it to the real-world device. The key to ensuring the translation went smoothly was injecting random noise into the simulation to mimic some of the unpredictability of the real world.
And just a couple of weeks ago, MIT researchers demonstrated a new technique that let a robot arm learn to manipulate new objects with far less training data than is usually required. By getting the algorithm to focus on a few key points on the object necessary for picking it up, the system could learn to pick up a previously unseen object after seeing only a few dozen examples (rather than the hundreds or thousands typically required).
How quickly these innovations will trickle down to practical applications remains to be seen, but a number of startups as well as logistics behemoth Amazon are developing robots designed to flexibly pick and place the wide variety of items found in your average warehouse.
Whether the economics of using robots to replace humans at these kinds of menial tasks makes sense yet is still unclear. The collapse of collaborative robotics pioneer Rethink Robotics last year suggests there are still plenty of challenges.
But at the same time, the number of robotic warehouses is expected to leap from 4,000 today to 50,000 by 2025. It may not be long until robots are muscling in on tasks we’ve long assumed only humans could do.
Image Credit: Visual Generation / Shutterstock.com Continue reading