Tag Archives: program
#437193 TyroBot: DIY Humanoid Robot
“TyroBot” is a novice-friendly (but high-tech) 3D-printable open source kit that can be assembled in a few hours, and is very easy to program. It’s great way to teach yourself robotics and programming! Read more, or back them on Kickstarter.
Posted in Human Robots
#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
#437982 Superintelligent AI May Be Impossible to ...
It may be theoretically impossible for humans to control a superintelligent AI, a new study finds. Worse still, the research also quashes any hope for detecting such an unstoppable AI when it’s on the verge of being created.
Slightly less grim is the timetable. By at least one estimate, many decades lie ahead before any such existential computational reckoning could be in the cards for humanity.
Alongside news of AI besting humans at games such as chess, Go and Jeopardy have come fears that superintelligent machines smarter than the best human minds might one day run amok. “The question about whether superintelligence could be controlled if created is quite old,” says study lead author Manuel Alfonseca, a computer scientist at the Autonomous University of Madrid. “It goes back at least to Asimov’s First Law of Robotics, in the 1940s.”
The Three Laws of Robotics, first introduced in Isaac Asimov's 1942 short story “Runaround,” are as follows:
A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.
In 2014, philosopher Nick Bostrom, director of the Future of Humanity Institute at the University of Oxford, not only explored ways in which a superintelligent AI could destroy us but also investigated potential control strategies for such a machine—and the reasons they might not work.
Bostrom outlined two possible types of solutions of this “control problem.” One is to control what the AI can do, such as keeping it from connecting to the Internet, and the other is to control what it wants to do, such as teaching it rules and values so it would act in the best interests of humanity. The problem with the former is that Bostrom thought a supersmart machine could probably break free from any bonds we could make. With the latter, he essentially feared that humans might not be smart enough to train a superintelligent AI.
Now Alfonseca and his colleagues suggest it may be impossible to control a superintelligent AI, due to fundamental limits inherent to computing itself. They detailed their findings this month in the Journal of Artificial Intelligence Research.
The researchers suggested that any algorithm that sought to ensure a superintelligent AI cannot harm people had to first simulate the machine’s behavior to predict the potential consequences of its actions. This containment algorithm then would need to halt the supersmart machine if it might indeed do harm.
However, the scientists said it was impossible for any containment algorithm to simulate the AI’s behavior and predict with absolute certainty whether its actions might lead to harm. The algorithm could fail to correctly simulate the AI’s behavior or accurately predict the consequences of the AI’s actions and not recognize such failures.
“Asimov’s first law of robotics has been proved to be incomputable,” Alfonseca says, “and therefore unfeasible.”
We may not even know if we have created a superintelligent machine, the researchers say. This is a consequence of Rice’s theorem, which essentially states that one cannot in general figure anything out about what a computer program might output just by looking at the program, Alfonseca explains.
On the other hand, there’s no need to spruce up the guest room for our future robot overlords quite yet. Three important caveats to the research still leave plenty of uncertainty to the group’s predictions.
First, Alfonseca estimates AI’s moment of truth remains, he says, “At least two centuries in the future.”
Second, he says researchers do not know if so-called artificial general intelligence, also known as strong AI, is theoretically even feasible. “That is, a machine as intelligent as we are in an ample variety of fields,” Alfonseca explains.
Last, Alfonseca says, “We have not proved that superintelligences can never be controlled—only that they can’t always be controlled.”
Although it may not be possible to control a superintelligent artificial general intelligence, it should be possible to control a superintelligent narrow AI—one specialized for certain functions instead of being capable of a broad range of tasks like humans. “We already have superintelligences of this type,” Alfonseca says. “For instance, we have machines that can compute mathematics much faster than we can. This is [narrow] superintelligence, isn’t it?” Continue reading
Posted in Human Robots