Tag Archives: order
#437807 Why We Need Robot Sloths
An inherent characteristic of a robot (I would argue) is embodied motion. We tend to focus on motion rather a lot with robots, and the most dynamic robots get the most attention. This isn’t to say that highly dynamic robots don’t deserve our attention, but there are other robotic philosophies that, while perhaps less visually exciting, are equally valuable under the right circumstances. Magnus Egerstedt, a robotics professor at Georgia Tech, was inspired by some sloths he met in Costa Rica to explore the idea of “slowness as a design paradigm” through an arboreal robot called SlothBot.
Since the robot moves so slowly, why use a robot at all? It may be very energy-efficient, but it’s definitely not more energy efficient than a static sensing system that’s just bolted to a tree or whatever. The robot moves, of course, but it’s also going to be much more expensive (and likely much less reliable) than a handful of static sensors that could cover a similar area. The problem with static sensors, though, is that they’re constrained by power availability, and in environments like under a dense tree canopy, you’re not going to be able to augment their lifetime with solar panels. If your goal is a long-duration study of a small area (over weeks or months or more), SlothBot is uniquely useful in this context because it can crawl out from beneath a tree to find some sun to recharge itself, sunbathe for a while, and then crawl right back again to resume collecting data.
SlothBot is such an interesting concept that we had to check in with Egerstedt with a few more questions.
IEEE Spectrum: Tell us what you find so amazing about sloths!
Magnus Egerstedt: Apart from being kind of cute, the amazing thing about sloths is that they have carved out a successful ecological niche for themselves where being slow is not only acceptable but actually beneficial. Despite their pretty extreme low-energy lifestyle, they exhibit a number of interesting and sometimes outright strange behaviors. And, behaviors having to do with territoriality, foraging, or mating look rather different when you are that slow.
Are you leveraging the slothiness of the design for this robot somehow?
Sadly, the sloth design serves no technical purpose. But we are also viewing the SlothBot as an outreach platform to get kids excited about robotics and/or conservation biology. And having the robot look like a sloth certainly cannot hurt.
“Slowness is ideal for use cases that require a long-term, persistent presence in an environment, like for monitoring tasks. I can imagine slow robots being out on farm fields for entire growing cycles, or suspended on the ocean floor keeping track of pollutants or temperature variations.”
—Magnus Egerstedt, Georgia Tech
Can you talk more about slowness as a design paradigm?
The SlothBot is part of a broader design philosophy that I have started calling “Robot Ecology.” In ecology, the connections between individuals and their environments/habitats play a central role. And the same should hold true in robotics. The robot design must be understood in the environmental context in which it is to be deployed. And, if your task is to be present in a slowly varying environment over a long time scale, being slow seems like the right way to go. Slowness is ideal for use cases that require a long-term, persistent presence in an environment, like for monitoring tasks, where the environment itself is slowly varying. I can imagine slow robots being out on farm fields for entire growing cycles, or suspended on the ocean floor keeping track of pollutants or temperature variations.
How do sloths inspire SlothBot’s functionality?
Its motions are governed by what we call survival constraints. These constraints ensure that the SlothBot is always able to get to a sunny spot to recharge. The actual performance objective that we have given to the robot is to minimize energy consumption, i.e., to simply do nothing subject to the survival constraints. The majority of the time, the robot simply sits there under the trees, measuring various things, seemingly doing absolutely nothing and being rather sloth-like. Whenever the SlothBot does move, it does not move according to some fixed schedule. Instead, it moves because it has to in order to “survive.”
How would you like to improve SlothBot?
I have a few directions I would like to take the SlothBot. One is to make the sensor suites richer to make sure that it can become a versatile and useful science instrument. Another direction involves miniaturization – I would love to see a bunch of small SlothBots “living” among the trees somewhere in a rainforest for years, providing real-time data as to what is happening to the ecosystem. Continue reading
#437701 Robotics, AI, and Cloud Computing ...
IBM must be brimming with confidence about its new automated system for performing chemical synthesis because Big Blue just had twenty or so journalists demo the complex technology live in a virtual room.
IBM even had one of the journalists choose the molecule for the demo: a molecule in a potential Covid-19 treatment. And then we watched as the system synthesized and tested the molecule and provided its analysis in a PDF document that we all saw in the other journalist’s computer. It all worked; again, that’s confidence.
The complex system is based upon technology IBM started developing three years ago that uses artificial intelligence (AI) to predict chemical reactions. In August 2018, IBM made this service available via the Cloud and dubbed it RXN for Chemistry.
Now, the company has added a new wrinkle to its Cloud-based AI: robotics. This new and improved system is no longer named simply RXN for Chemistry, but RoboRXN for Chemistry.
All of the journalists assembled for this live demo of RoboRXN could watch as the robotic system executed various steps, such as moving the reactor to a small reagent and then moving the solvent to a small reagent. The robotic system carried out the entire set of procedures—completing the synthesis and analysis of the molecule—in eight steps.
Image: IBM Research
IBM RXN helps predict chemical reaction outcomes or design retrosynthesis in seconds.
In regular practice, a user will be able to suggest a combination of molecules they would like to test. The AI will pick up the order and task a robotic system to run the reactions necessary to produce and test the molecule. Users will be provided analyses of how well their molecules performed.
Back in March of this year, Silicon Valley-based startup Strateos demonstrated something similar that they had developed. That system also employed a robotic system to help researchers working from the Cloud create new chemical compounds. However, what distinguishes IBM’s system is its incorporation of a third element: the AI.
The backbone of IBM’s AI model is a machine learning translation method that treats chemistry like language translation. It translates the language of chemistry by converting reactants and reagents to products through the use of Statistical Machine Intelligence and Learning Engine (SMILE) representation to describe chemical entities.
IBM has also leveraged an automatic data driven strategy to ensure the quality of its data. Researchers there used millions of chemical reactions to teach the AI system chemistry, but contained within that data set were errors. So, how did IBM clean this so-called noisy data to eliminate the potential for bad models?
According to Alessandra Toniato, a researcher at IBM Zurichh, the team implemented what they dubbed the “forgetting experiment.”
Toniato explains that, in this approach, they asked the AI model how sure it was that the chemical examples it was given were examples of correct chemistry. When faced with this choice, the AI identified chemistry that it had “never learnt,” “forgotten six times,” or “never forgotten.” Those that were “never forgotten” were examples that were clean, and in this way they were able to clean the data that AI had been presented.
While the AI has always been part of the RXN for Chemistry, the robotics is the newest element. The main benefit that turning over the carrying out of the reactions to a robotic system is expected to yield is to free up chemists from doing the often tedious process of having to design a synthesis from scratch, says Matteo Manica, a research staff member in Cognitive Health Care and Life Sciences at IBM Research Zürich.
“In this demo, you could see how the system is synergistic between a human and AI,” said Manica. “Combine that with the fact that we can run all these processes with a robotic system 24/7 from anywhere in the world, and you can see how it will really help up to speed up the whole process.”
There appear to be two business models that IBM is pursuing with its latest technology. One is to deploy the entire system on the premises of a company. The other is to offer licenses to private Cloud installations.
Photo: Michael Buholzer
Teodoro Laino of IBM Research Europe.
“From a business perspective you can think of having a system like we demonstrated being replicated on the premise within companies or research groups that would like to have the technology available at their disposal,” says Teodoro Laino, distinguished RSM, manager at IBM Research Europe. “On the other hand, we are also pushing at bringing the entire system to a service level.”
Just as IBM is brimming with confidence about its new technology, the company also has grand aspirations for it.
Laino adds: “Our aim is to provide chemical services across the world, a sort of Amazon of chemistry, where instead of looking for chemistry already in stock, you are asking for chemistry on demand.”
< Back to IEEE COVID-19 Resources Continue reading