In this episode of Robots in Depth, Per Sjöborg speaks with Søren Peter Johansen from DTI about implementing robotic solutions.
Søren talks about how he got into robotics by starting to tinker with any electronic he could get his hands on. He worked in a mechanical workshop and added automation to the machines in the shop. As robots became more and more available, he then included them in his work.
Søren also discusses examples of successful human robot collaboration and how software and hardware both are essential elements of robot development.
We also get to hear about how he went to the Danish Technological Institute because he saw an opportunity to work with lots of interesting robots.
This interview was recorded in 2016.
We are excited to announce the AI Driving Olympics (AI-DO), a new competition focused around AI for self-driving cars. The first edition is going to be at NIPS 2018; the second edition will be at ICRA 2019.
The competition comprises challenges of increasing complexity for self-driving cars, from lane following to fleet management, all on the Duckietown platform.
We are excited because AI-DO at NIPS 2018 is going to be the first robotic competition at a machine learning conference, and in a way that is completely reproducible: you send your code — we run it on our robots. Or, you can get a Duckiebot for yourself through the Kickstarter run by our newly-established non-profit Duckietown foundation.
For all details and rules about AI-DO see here.
The AI Driving Olympics is presented in collaboration with 6 academic institutions: ETH Zürich (Switzerland), Université de Montréal (Canada), National Chiao Tung University (Taiwan), Toyota Technological Institute at Chicago (USA), Tsinghua University (China) and Georgia Tech (USA), as well as two industry co-organizers: nuTonomy (a self-driving car company) and Amazon Web Services (AWS).
The newsletter The Information reports Uber’s investors are pushing Uber to sell its self-drive division to some other large player. The division has, of course, been nothing but trouble for Uber, and as I have noted several times, Uber is one of the few large players in this space that doesn’t have to build their own tech. They have the #1 brand in selling rides, and selling rides is what the robotaxi business is all about.
At the same time, Uber recruited a great team (though it has lost many of them.) First they recruited many of the best around CMU for their Pittsburgh ATC headquarters. CMU and Stanford are where most of the stars of the robocar world have come from. Then they “paid” $680M in stock for Otto, which was really an acqui-hire in many ways, at least if you believe court documents. Because that stock payout required certain milestones that weren’t met, the Otto stockholders did not get their money, but Uber had to make a large stock payout to Waymo as a result of the lawsuit over Anthony Levanowski’s actions.
So they have this team, but the team and the Uber name are tarnished by the fatality. But the right pickings from the team are still valuable to somebody who is falling behind, if you eliminate the factors that led to the fatality and take the Uber name off it.
At the same time, Uber gets a pledge that the car the team makes will be usable in the Uber network. They get guaranteed access to tech (if the new owner succeeds) though not the deep control that comes with owning it. They don’t need the deep control. In fact, it’s now better if they can stick another famous brand on it.
Aside from Starship (which I am involved in) the company to watch in the delivery space is Nuro. Nuro has started a delivery pilot in Scottsdale with Fry’s groceries. The pilot will be done with regular Prius and Leaf cars with Nuro’s self drive tech, and a safety driver, not with the small no-seat delivery pod Nuro is trying to build. That pod isn’t ready for the real streets, even the easy streets of Scottsdale.
Nuro is the company to watch in on-road delivery because its founders were among the top members of Google/Waymo’s team. I worked with them regularly when I was there and they are the best. They have also raised lots of money.
Driving on the road is much harder than the sidewalk, though. A road robot must be able to handle any situation on its own and get to a safe state if there is a problem no matter what. A sidewalk robot can stop in 30cm. If it encounters something it doesn’t understand, it can just stop and request help from HQ. Safety is very unlikely to be an issue, while a road robot could really hurt somebody in a mistake.
The $6 price for delivery in the pilot is strange, since that’s a common price for human driven delivery.
By Adam Conner-Simons | Rachel Gordon
Investigating inside the human body often requires cutting open a patient or swalloing long tubes with built-in cameras. But what if physicians could get a better glimpse in a less expensive, invasive, and time-consuming manner?
A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) led by Professor Dina Katabi is working on doing exactly that with an “in-body GPS” system dubbed ReMix. The new method can pinpoint the location of ingestible implants inside the body using low-power wireless signals. These implants could be used as tiny tracking devices on shifting tumors to help monitor their slight movements.
In animal tests, the team demonstrated that they can track the implants with centimeter-level accuracy. The team says that, one day, similar implants could be used to deliver drugs to specific regions in the body.
ReMix was developed in collaboration with researchers from Massachusetts General Hospital (MGH). The team describes the system in a paper that’s being presented at this week’s Association for Computing Machinery’s Special Interest Group on Data Communications (SIGCOMM) conference in Budapest, Hungary.
Tracking inside the body
To test ReMix, Katabi’s group first implanted a small marker in animal tissues. To track its movement, the researchers used a wireless device that reflects radio signals off the patient. This was based on a wireless technology that the researchers previously demonstrated to detect heart rate, breathing, and movement. A special algorithm then uses that signal to pinpoint the exact location of the marker.
Interestingly, the marker inside the body does not need to transmit any wireless signal. It simply reflects the signal transmitted by the wireless device outside the body. Therefore, it doesn’t need a battery or any other external source of energy.
A key challenge in using wireless signals in this way is the many competing reflections that bounce off a person’s body. In fact, the signals that reflect off a person’s skin are actually 100 million times more powerful than the signals of the metal marker itself.
To overcome this, the team designed an approach that essentially separates the interfering skin signals from the ones they’re trying to measure. They did this using a small semiconductor device, called a “diode,” that mixes signals together so the team can then filter out the skin-related signals. For example, if the skin reflects at frequencies of F1 and F2, the diode creates new combinations of those frequencies, such as F1-F2 and F1+F2. When all of the signals reflect back to the system, the system only picks up the combined frequencies, filtering out the original frequencies that came from the patient’s skin.
One potential application for ReMix is in proton therapy, a type of cancer treatment that involves bombarding tumors with beams of magnet-controlled protons. The approach allows doctors to prescribe higher doses of radiation, but requires a very high degree of precision, which means that it’s usually limited to only certain cancers.
Its success hinges on something that’s actually quite unreliable: a tumor staying exactly where it is during the radiation process. If a tumor moves, then healthy areas could be exposed to the radiation. But with a small marker like ReMix’s, doctors could better determine the location of a tumor in real-time and either pause the treatment or steer the beam into the right position. (To be clear, ReMix is not yet accurate enough to be used in clinical settings. Katabi says a margin of error closer to a couple of millimeters would be necessary for actual implementation.)
“The ability to continuously sense inside the human body has largely been a distant dream,” says Romit Roy Choudhury, a professor of electrical engineering and computer science at the University of Illinois, who was not involved in the research. “One of the roadblocks has been wireless communication to a device and its continuous localization. ReMix makes a leap in this direction by showing that the wireless component of implantable devices may no longer be the bottleneck.”
Looking ahead
There are still many ongoing challenges for improving ReMix. The team next hopes to combine the wireless data with medical data, such as that from magnetic resonance imaging (MRI) scans, to further improve the system’s accuracy. In addition, the team will continue to reassess the algorithm and the various tradeoffs needed to account for the complexity of different bodies.
“We want a model that’s technically feasible, while still complex enough to accurately represent the human body,” says MIT PhD student Deepak Vasisht, lead author on the new paper. “If we want to use this technology on actual cancer patients one day, it will have to come from better modeling a person’s physical structure.”
The researchers say that such systems could help enable more widespread adoption of proton therapy centers. Today, there are only about 100 centers globally.
“One reason that [proton therapy] is so expensive is because of the cost of installing the hardware,” Vasisht says. “If these systems can encourage more applications of the technology, there will be more demand, which will mean more therapy centers, and lower prices for patients.”
Katabi and Vasisht co-wrote the paper with MIT PhD student Guo Zhang, University of Waterloo professor Omid Abari, MGH physicist Hsaio-Ming Lu, and MGH technical director Jacob Flanz.