Tag Archives: Performance
#438613 Video Friday: Digit Takes a Hike
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!):
HRI 2021 – March 8-11, 2021 – [Online Conference]
RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
Let us know if you have suggestions for next week, and enjoy today's videos.
It's winter in Oregon, so everything is damp, all the time. No problem for Digit!
Also the case for summer in Oregon.
[ Agility Robotics ]
While other organisms form collective flocks, schools, or swarms for such purposes as mating, predation, and protection, the Lumbriculus variegatus worms are unusual in their ability to braid themselves together to accomplish tasks that unconnected individuals cannot. A new study reported by researchers at the Georgia Institute of Technology describes how the worms self-organize to act as entangled “active matter,” creating surprising collective behaviors whose principles have been applied to help blobs of simple robots evolve their own locomotion.
No, this doesn't squick me out at all, why would it.
[ Georgia Tech ]
A few years ago, we wrote about Zhifeng Huang's jet-foot equipped bipedal robot, and he's been continuing to work on it to the point where it can now step over gaps that are an absolutely astonishing 147% of its leg length.
[ Paper ]
Thanks Zhifeng!
The Inception Drive is a novel, ultra-compact design for an Infinitely Variable Transmission (IVT) that uses nested-pulleys to adjust the gear ratio between input and output shafts. This video shows the first proof-of-concept prototype for a “Fully Balanced” design, where the spinning masses within the drive are completely balanced to reduce vibration, thereby allowing the drive to operate more efficiently and at higher speeds than achievable on an unbalanced design.
As shown in this video, the Inception Drive can change both the speed and direction of rotation of the output shaft while keeping the direction and speed of the input shaft constant. This ability to adjust speed and direction within such a compact package makes the Inception Drive a compelling choice for machine designers in a wide variety of fields, including robotics, automotive, and renewable-energy generation.
[ SRI ]
Robots with kinematic loops are known to have superior mechanical performance. However, due to these loops, their modeling and control is challenging, and prevents a more widespread use. In this paper, we describe a versatile Inverse Kinematics (IK) formulation for the retargeting of expressive motions onto mechanical systems with loops.
[ Disney Research ]
Watch Engineered Arts put together one of its Mesmer robots in a not at all uncanny way.
[ Engineered Arts ]
There's been a bunch of interesting research into vision-based tactile sensing recently; here's some from Van Ho at JAIST:
[ Paper ]
Thanks Van!
This is really more of an automated system than a robot, but these little levitating pucks are very very slick.
ACOPOS 6D is based on the principle of magnetic levitation: Shuttles with integrated permanent magnets float over the surface of electromagnetic motor segments. The modular motor segments are 240 x 240 millimeters in size and can be arranged freely in any shape. A variety of shuttle sizes carry payloads of 0.6 to 14 kilograms and reach speeds of up to 2 meters per second. They can move freely in two-dimensional space, rotate and tilt along three axes and offer precise control over the height of levitation. All together, that gives them six degrees of motion control freedom.
[ ACOPOS ]
Navigation and motion control of a robot to a destination are tasks that have historically been performed with the assumption that contact with the environment is harmful. This makes sense for rigid-bodied robots where obstacle collisions are fundamentally dangerous. However, because many soft robots have bodies that are low-inertia and compliant, obstacle contact is inherently safe. We find that a planner that takes into account and capitalizes on environmental contact produces paths that are more robust to uncertainty than a planner that avoids all obstacle contact.
[ CHARM Lab ]
The quadrotor experts at UZH have been really cranking it up recently.
Aerodynamic forces render accurate high-speed trajectory tracking with quadrotors extremely challenging. These complex aerodynamic effects become a significant disturbance at high speeds, introducing large positional tracking errors, and are extremely difficult to model. To fly at high speeds, feedback control must be able to account for these aerodynamic effects in real-time. This necessitates a modelling procedure that is both accurate and efficient to evaluate. Therefore, we present an approach to model aerodynamic effects using Gaussian Processes, which we incorporate into a Model Predictive Controller to achieve efficient and precise real-time feedback control, leading to up to 70% reduction in trajectory tracking error at high speeds. We verify our method by extensive comparison to a state-of-the-art linear drag model in synthetic and real-world experiments at speeds of up to 14m/s and accelerations beyond 4g.
[ Paper ]
I have not heard much from Harvest Automation over the last couple years and their website was last updated in 2016, but I guess they're selling robots in France, so that's good?
[ Harvest Automation ]
Last year, Clearpath Robotics introduced a ROS package for Spot which enables robotics developers to leverage ROS capabilities out-of-the-box. Here at OTTO Motors, we thought it would be a compelling test case to see just how easy it would be to integrate Spot into our test fleet of OTTO materials handling robots.
[ OTTO Motors ]
Video showcasing recent robotics activities at PRISMA Lab, coordinated by Prof. Bruno Siciliano, at Università di Napoli Federico II.
[ PRISMA Lab ]
Thanks Fan!
State estimation framework developed by the team CoSTAR for the DARPA Subterranean Challenge, where the team achieved 2nd and 1st places in the Tunnel and Urban circuits.
[ Paper ]
Highlights from the 2020 ROS Industrial conference.
[ ROS Industrial ]
Thanks Thilo!
Not robotics, but entertaining anyway. From the CHI 1995 Technical Video Program, “The Tablet Newspaper: a Vision for the Future.”
[ CHI 1995 ]
This week's GRASP on Robotics seminar comes from Allison Okamura at Stanford, on “Wearable Haptic Devices for Ubiquitous Communication.”
Haptic devices allow touch-based information transfer between humans and intelligent systems, enabling communication in a salient but private manner that frees other sensory channels. For such devices to become ubiquitous, their physical and computational aspects must be intuitive and unobtrusive. We explore the design of a wide array of haptic feedback mechanisms, ranging from devices that can be actively touched by the fingertips to multi-modal haptic actuation mounted on the arm. We demonstrate how these devices are effective in virtual reality, human-machine communication, and human-human communication.
[ UPenn ] Continue reading
Posted in Human Robots
#438553 New Drone Software Handles Motor ...
Good as some drones are becoming at obstacle avoidance, accidents do still happen. And as far as robots go, drones are very much on the fragile side of things. Any sort of significant contact between a drone and almost anything else usually results in a catastrophic, out-of-control spin followed by a death plunge to the ground. Bad times. Bad, expensive times.
A few years ago, we saw some interesting research into software that can keep the most common drone form factor, the quadrotor, aloft and controllable even after the failure of one motor. The big caveat to that software was that it relied on GPS for state estimation, meaning that without a GPS signal, the drone is unable to get the information it needs to keep itself under control. In a paper recently accepted to RA-L, researchers at the University of Zurich report that they have developed a vision-based system that brings state estimation completely on-board. The upshot: potentially any drone with some software and a camera can keep itself safe even under the most challenging conditions.
A few years ago, we wrote about first author Sihao Sun’s work on high speed controlled flight of a quadrotor with a non-functional motor. But that innovation relied on an external motion capture system. Since then, Sun has moved from Tu Delft to Davide Scaramuzza’s lab at UZH, and it looks like he’s been able to combine his work on controlled spinning flight with the Robotics and Perception Group’s expertise in vision. Now, a downward-facing camera is all it takes for a spinning drone to remain stable and controllable:
Remember, this software isn’t just about guarding against motor failure. Drone motors themselves don’t just up and fail all that often, either with respect to their software or hardware. But they do represent the most likely point of failure for any drone, usually because when you run into something, what ultimately causes your drone to crash is damage to a motor or a propeller that causes loss of control.
The reason that earlier solutions relied on GPS was because the spinning drone needs a method of state estimation—that is, in order to be closed-loop controllable, the drone needs to have a reasonable understanding of what its position is and how that position is changing over time. GPS is an easy way to take care of this, but GPS is also an external system that doesn’t work everywhere. Having a state estimation system that’s completely internal to the drone itself is much more fail safe, and Sun got his onboard system to work through visual feature tracking with a downward-facing camera, even as the drone is spinning at over 20 rad/s.
While the system works well enough with a regular downward-facing camera—something that many consumer drones are equipped with for stabilization purposes—replacing it with an event camera (you remember event cameras, right?) makes the performance even better, especially in low light.
For more details on this, including what you’re supposed to do with a rapidly spinning partially disabled quadrotor (as well as what it’ll take to make this a standard feature on consumer hardware), we spoke with Sihao Sun via email.
IEEE Spectrum: what usually happens when a drone spinning this fast lands? Is there any way to do it safely?
Sihao Sun: Our experience shows that we can safely land the drone while it is spinning. When the range sensor measurements are lower than a threshold (around 10 cm, indicating that the drone is close to the ground), we switch off the rotors. During the landing procedure, despite the fast spinning motion, the thrust direction oscillates around the gravity vector, thus the drone touches the ground with its legs without damaging other components.
Can your system handle more than one motor failure?
Yes, the system can also handle the failure of two opposing rotors. However, if two adjacent rotors or more than two rotors fail, our method cannot save the quadrotor. Some research has shown that it is possible to control a quadrotor with only one remaining rotor. But the drone requires a very special inertial property, which is hard to satisfy in real applications.
How different is your system's performance from a similar system that relies on GPS, in a favorable environment?
In a favorable environment, our system outperforms those relying on GPS signals because it obtains better position estimates. Since a damaged quadrotor spins fast, the accelerometer readings are largely affected by centrifugal forces. When the GPS signal is lost or degraded, a drone relying on GPS needs to integrate these biased accelerometer measurements for position estimation, leading to large position estimation errors. Feeding these erroneous estimates to the flight controller can easily crash the drone.
When you say that your solution requires “only onboard sensors and computation,” are those requirements specialized, or would they be generally compatible with the current generation of recreational and commercial quadrotors?
We use an NVIDIA Jetson TX2 to run our solution, which includes two parts: the control algorithm and the vision-based state estimation algorithm. The control algorithm is lightweight; thus, we believe that it is compatible with the current generation of quadrotors. On the other hand, the vision-based state estimation requires relatively more computational resources, which may not be affordable for cheap recreational platforms. But this is not an issue for commercial quadrotors because many of them have more powerful processors than a TX2.
What else can event cameras be used for, in recreational or commercial applications?
Many drone applications can benefit from event cameras, especially those in high-speed or low-light conditions, such as autonomous drone racing, cave exploration, drone delivery during night time, etc. Event cameras also consume very little power, which is a significant advantage for energy-critical missions, such as planetary aerial vehicles for Mars explorations. Regarding space applications, we are currently collaborating with JPL to explore the use of event cameras to address the key limitations of standard cameras for the next Mars helicopter.
[ UZH RPG ] Continue reading
Posted in Human Robots
#438014 Meet Blueswarm, a Smart School of ...
Anyone who’s seen an undersea nature documentary has marveled at the complex choreography that schooling fish display, a darting, synchronized ballet with a cast of thousands.
Those instinctive movements have inspired researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), and the Wyss Institute for Biologically Inspired Engineering. The results could improve the performance and dependability of not just underwater robots, but other vehicles that require decentralized locomotion and organization, such as self-driving cars and robotic space exploration.
The fish collective called Blueswarm was created by a team led by Radhika Nagpal, whose lab is a pioneer in self-organizing systems. The oddly adorable robots can sync their movements like biological fish, taking cues from their plastic-bodied neighbors with no external controls required. Nagpal told IEEE Spectrum that this marks a milestone, demonstrating complex 3D behaviors with implicit coordination in underwater robots.
“Insights from this research will help us develop future miniature underwater swarms that can perform environmental monitoring and search in visually-rich but fragile environments like coral reefs,” Nagpal said. “This research also paves a way to better understand fish schools, by synthetically recreating their behavior.”
The research is published in Science Robotics, with Florian Berlinger as first author. Berlinger said the “Bluedot” robots integrate a trio of blue LED lights, a lithium-polymer battery, a pair of cameras, a Raspberry Pi computer and four controllable fins within a 3D-printed hull. The fish-lens cameras detect LED’s of their fellow swimmers, and apply a custom algorithm to calculate distance, direction and heading.
Based on that simple production and detection of LED light, the team proved that Blueswarm could self-organize behaviors, including aggregation, dispersal and circle formation—basically, swimming in a clockwise synchronization. Researchers also simulated a successful search mission, an autonomous Finding Nemo. Using their dispersion algorithm, the robot school spread out until one could detect a red light in the tank. Its blue LEDs then flashed, triggering the aggregation algorithm to gather the school around it. Such a robot swarm might prove valuable in search-and-rescue missions at sea, covering miles of open water and reporting back to its mates.
“Each Bluebot implicitly reacts to its neighbors’ positions,” Berlinger said. The fish—RoboCod, perhaps?—also integrate a Wifi module to allow uploading new behaviors remotely. The lab’s previous efforts include a 1,000-strong army of “Kilobots,” and a robotic construction crew inspired by termites. Both projects operated in two-dimensional space. But a 3D environment like air or water posed a tougher challenge for sensing and movement.
In nature, Berlinger notes, there’s no scaly CEO to direct the school’s movements. Nor do fish communicate their intentions. Instead, so-called “implicit coordination” guides the school’s collective behavior, with individual members executing high-speed moves based on what they see their neighbors doing. That decentralized, autonomous organization has long fascinated scientists, including in robotics.
“In these situations, it really benefits you to have a highly autonomous robot swarm that is self-sufficient. By using implicit rules and 3D visual perception, we were able to create a system with a high degree of autonomy and flexibility underwater where things like GPS and WiFi are not accessible.”
Berlinger adds the research could one day translate to anything that requires decentralized robots, from self-driving cars and Amazon warehouse vehicles to exploration of faraway planets, where poor latency makes it impossible to transmit commands quickly. Today’s semi-autonomous cars face their own technical hurdles in reliably sensing and responding to their complex environments, including when foul weather obscures onboard sensors or road markers, or when they can’t fix position via GPS. An entire subset of autonomous-car research involves vehicle-to-vehicle (V2V) communications that could give cars a hive mind to guide individual or collective decisions— avoiding snarled traffic, driving safely in tight convoys, or taking group evasive action during a crash that’s beyond their sensory range.
“Once we have millions of cars on the road, there can’t be one computer orchestrating all the traffic, making decisions that work for all the cars,” Berlinger said.
The miniature robots could also work long hours in places that are inaccessible to humans and divers, or even large tethered robots. Nagpal said the synthetic swimmers could monitor and collect data on reefs or underwater infrastructure 24/7, and work into tiny places without disturbing fragile equipment or ecosystems.
“If we could be as good as fish in that environment, we could collect information and be non-invasive, in cluttered environments where everything is an obstacle,” Nagpal said. Continue reading
Posted in Human Robots
#437978 How Mirroring the Architecture of the ...
While AI can carry out some impressive feats when trained on millions of data points, the human brain can often learn from a tiny number of examples. New research shows that borrowing architectural principles from the brain can help AI get closer to our visual prowess.
The prevailing wisdom in deep learning research is that the more data you throw at an algorithm, the better it will learn. And in the era of Big Data, that’s easier than ever, particularly for the large data-centric tech companies carrying out a lot of the cutting-edge AI research.
Today’s largest deep learning models, like OpenAI’s GPT-3 and Google’s BERT, are trained on billions of data points, and even more modest models require large amounts of data. Collecting these datasets and investing the computational resources to crunch through them is a major bottleneck, particularly for less well-resourced academic labs.
It also means today’s AI is far less flexible than natural intelligence. While a human only needs to see a handful of examples of an animal, a tool, or some other category of object to be able pick it out again, most AI need to be trained on many examples of an object in order to be able to recognize it.
There is an active sub-discipline of AI research aimed at what is known as “one-shot” or “few-shot” learning, where algorithms are designed to be able to learn from very few examples. But these approaches are still largely experimental, and they can’t come close to matching the fastest learner we know—the human brain.
This prompted a pair of neuroscientists to see if they could design an AI that could learn from few data points by borrowing principles from how we think the brain solves this problem. In a paper in Frontiers in Computational Neuroscience, they explained that the approach significantly boosts AI’s ability to learn new visual concepts from few examples.
“Our model provides a biologically plausible way for artificial neural networks to learn new visual concepts from a small number of examples,” Maximilian Riesenhuber, from Georgetown University Medical Center, said in a press release. “We can get computers to learn much better from few examples by leveraging prior learning in a way that we think mirrors what the brain is doing.”
Several decades of neuroscience research suggest that the brain’s ability to learn so quickly depends on its ability to use prior knowledge to understand new concepts based on little data. When it comes to visual understanding, this can rely on similarities of shape, structure, or color, but the brain can also leverage abstract visual concepts thought to be encoded in a brain region called the anterior temporal lobe (ATL).
“It is like saying that a platypus looks a bit like a duck, a beaver, and a sea otter,” said paper co-author Joshua Rule, from the University of California Berkeley.
The researchers decided to try and recreate this capability by using similar high-level concepts learned by an AI to help it quickly learn previously unseen categories of images.
Deep learning algorithms work by getting layers of artificial neurons to learn increasingly complex features of an image or other data type, which are then used to categorize new data. For instance, early layers will look for simple features like edges, while later ones might look for more complex ones like noses, faces, or even more high-level characteristics.
First they trained the AI on 2.5 million images across 2,000 different categories from the popular ImageNet dataset. They then extracted features from various layers of the network, including the very last layer before the output layer. They refer to these as “conceptual features” because they are the highest-level features learned, and most similar to the abstract concepts that might be encoded in the ATL.
They then used these different sets of features to train the AI to learn new concepts based on 2, 4, 8, 16, 32, 64, and 128 examples. They found that the AI that used the conceptual features yielded much better performance than ones trained using lower-level features on lower numbers of examples, but the gap shrunk as they were fed more training examples.
While the researchers admit the challenge they set their AI was relatively simple and only covers one aspect of the complex process of visual reasoning, they said that using a biologically plausible approach to solving the few-shot problem opens up promising new avenues in both neuroscience and AI.
“Our findings not only suggest techniques that could help computers learn more quickly and efficiently, they can also lead to improved neuroscience experiments aimed at understanding how people learn so quickly, which is not yet well understood,” Riesenhuber said.
As the researchers note, the human visual system is still the gold standard when it comes to understanding the world around us. Borrowing from its design principles might turn out to be a profitable direction for future research.
Image Credit: Gerd Altmann from Pixabay Continue reading
Posted in Human Robots