Category robots in business

Researchers from NCCR Robotics at the University of Zurich and Intel developed an algorithm that pushes autonomous drones to their physical limit.
Since the dawn of flight, acrobatics has been a way for pilots to prove their bravery and worth. It is also a way to push the envelope of what can be done with an aircraft, learning lessons that are useful to all pilots and engineers. The same is true for unmanned flight. Professional drone pilots perform acrobatic maneuvers in dedicated competitions, pushing drones to their physical limits and perfecting their control and efficiency.

Now a collaboration between researchers from the University of Zurich (part of the NCCR Robotics consortium) and Intel has developed a quadcopter that can learn to fly acrobatics autonomously, paving the way to drones that can fully exploit their agility and speed, and cover more distance within their battery life. Though no drone mission will probably ever require a power loop or a Matty flip – the typical acrobatic maneuvers – a drone that can perform them autonomously is likely to be more efficient at all times.

A step forward towards integrating drones in our everyday life

Researchers of the University of Zurich and Intel developed a novel algorithm that pushes autonomous drones with only on-board sensing and computation close to their physical limits. To prove the efficiency of the developed algorithm, the researchers made an autonomous quadrotor fly acrobatic maneuvers such as the Power Loop, the Barrel Roll, and the Matty Flip, during which the drone incurs accelerations of up to 3g. “Several applications of drones, such as search-and-rescue or delivery, will strongly benefit from faster drones, which can cover large distances in limited time. With this algorithm we have taken a step forward towards integrating autonomously navigating drones into our everyday life”, says Davide Scaramuzza, Professor and Director of the Robotics and Perception Group at the University of Zurich, and head of the Rescue Robotics Grand Challenge for NCCR Robotics.

Simulation for training, real-world for testing
The navigation algorithm that allows drones to fly acrobatic maneuvers is represented by an artificial neural network that directly converts observations from the on-board camera and inertial sensors, to control commands. This neural network is trained exclusively in simulation. Learning agile maneuvers entirely in simulation has several advantages: (i) Maneuvers can be simply specified by reference trajectories in simulation and do not require expensive demonstrations by a human pilot, (ii) training is safe and does not pose any physical risk to the quadrotor, and (iii) the approach can scale to a large number of diverse maneuvers, including ones that can only be performed by the very best human pilots.

The algorithm transfers its knowledge to reality by using appropriate abstractions of the visual and inertial inputs (i.e., feature tracks and integrated inertial measurements), which decreases the gap between the simulated and physical world. Indeed, without physically-accurate modeling of the world or any fine-tuning on real-world data, the trained neural network can be deployed on a real quadrotor to perform acrobatic maneuvers.

Towards fully autonomous drones
Within a few hours of training in simulation, our algorithm learns to fly acrobatic maneuvers with an accuracy comparable to professional human pilots. Nevertheless, the research team warns that there is still a significant gap between what human pilots and autonomous drones can do. “The best human pilots still have an edge over autonomous drones given their ability to quickly interpret and adapt to unexpected situations and changes in the environment,” says Prof. Scaramuzza.

Paper: E. Kaufmann*, A. Loquercio*, R. Ranftl, M. Müller, V. Koltun, D. Scaramuzza “Deep Drone Acrobatics”, Robotics: Science and Systems (RSS), 2020
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What are the main reasons that robotics companies and startups fail? Is it the technology or is it the business? Fresh Consulting analyzed significant industry case studies from Rethink Robotics to iRobot for their whitepaper “Why Robotics Companies Fail,” and launched it on June 11 at a panel discussion moderated by James Dietrich, from Fresh Consulting, with guest speakers Aaron Prather, Senior Advisor for the Technology Research and Planning Team at FedEx Express; Andra Keay, Managing Director of Silicon Valley Robotics and startup accelerator advisor, and Eric Klein, Partner and Founder at Lemnos Labs.

In a lively discussion, the speakers weighed in on what key factors for success or failure were most likely in their experience. Andra Keay believes that lack of business fundamentals is the most critical error a young company faces. “There are a lot of resources out there to help startups pitch, value their company, draw up cap tables and divide equity. We’ve never had such a well educated set of founders from that perspective. But still simple business fundamentals bring down startups, whereas serial entrepreneurs know how to avoid a lot of those pitfalls.”

As Eric Klein continued, “We’re entering a golden era for applied robotics. It’s never been easier or cheaper from a technological standpoint to build robotics companies. But from a venture capital perspective there are still business barriers, particularly in understanding the customer’s economics.”

From a wealth of experience at FedEx working with robotics companies, Aaron Prather agrees that customer knowledge and understanding was the most significant point of failure from startups. “One of the things that we’re really big on at FedEx is seeing if you are matching technology to the right use case. How well do you know your customer’s problems and needs?”

According to the whitepaper, the five central themes that are consistent among failed robotics companies are:

• lacking in business fundamentals,
• poor market fit and timing,
• bad user experience and integration,
• misaligned investors and partners
• and focusing on the wrong problem.

Is it the investor or the startup who should take the blame for failure? The panelists all agreed that misalignment is the key issue. Keay said “it’s honestly scary that startups still need to be told to ask their investors questions! It’s not just about getting money, it’s about finding the right partners for your company’s growth. Some investors are hands-on, some are not, which is fine. Startups need to ask questions about what value they’re getting out of the deal.”

According to Klein, “if you don’t have strong business fundamentals or you’re doing all your learning real time, then that’s like driving into the fog of war and hoping to find your way out. Your selection of a venture capitalist is a two way relationship. A serial entrepreneur may say, Hey, I know a lot of what I need to do and what I need is access to silent capital. But for other folks it may be their first time, even if they’re an amazing crew of mechatronics folks coming out of the best schools on a global basis, Waterloo or Tel Aviv or Shenzhen or Sydney. Wherever you come from, your mechatronics degree means you can build the product. And the first risk any venture capitalist is trying to disassemble is engineering risk. But the second equally important risk is product market fit”

The discussion continued with the pros and cons of the Robot-As-A-Service business model, which is becoming very popular in the venture capital community but doesn’t make as much sense for some larger customers, for example like Fedex, who are happy to put robots on the capex budget, not the opex budget. If the customer is always right, then a startup needs to adapt their business model, in spite of push from the investors eager for ARR (annual returning revenue), or similar business models borrowed from the software industry.

Another discussion point was that finding the right metrics for success for each company is still a black art. Is it volume? Is it revenue? Is it number of customers? And the more innovative you are, the harder it might be to arrive at the right metrics. Keay suggested Series C funding as one metric in proxy for number of deployments, and Klein pointed out that in his opinion most robotics startups were seriously undervalued at a Series B and C stage at the moment, because investors don’t yet place sufficient value on the ability to produce multiple physical robots.

Final comments from the panel included request for startups to get in touch. Both Fedex, Lemnos Labs and Silicon Valley Robotics are there to share knowledge that can help startups succeed, and as Klein said, “Between the three of us as panelists we have this collective scar tissue from having done this for a while. And I want you to make spectacular mistakes. Don’t make the silly mistakes I made as an entrepreneur for 20 years, make a new spectacular one. So network in the robotics community.”

We hope you enjoyed this in-depth discussion on the topic of “Why Robotics Companies Succeed or Fail” as much as we did. In case you missed it, we’ve made the webinar recording available for you to watch online! Click here to view the recording.

If you haven’t already, please use this link to download our newest white paper, “Why Robotics Companies Fail,” which provides a deeper exploration into the topics and themes we discussed with our panelists.

Imagine a dressing that releases antibiotics on demand and absorbs excessive wound exudate at the same time. Researchers at Eindhoven University of Technology hope to achieve just that, by developing a smart coating that actively releases and absorbs multiple fluids, triggered by a radio signal. This material is not only beneficial for the health care industry, it is also very promising in the field of robotics or even virtual reality.

Universal Robots’ VP of Operations and Supply Chain, Martin Kjærbo shares with us his thoughts on how Honeywell Intelligrated is managing through the pandemic as well as insights into what the future of Robotics and Automation may be.

A navigation algorithm developed at the University of Zurich enables drones to learn challenging acrobatic maneuvers. Autonomous quadcopters can be trained using simulations to increase their speed, agility and efficiency, which benefits conventional search and rescue operations.

The introduction of construction robotics may prove to be a tipping point for this industry. How are robots changing on-site construction today, and where can we expect to see them in the future?

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In this episode, we hear from Luca Colasanto, Senior Robotic Scientist at Realtime Robotics, about real-time robot motion planning in dynamic and complex environments with human-robot collaboration. Realtime Robotics focuses on accelerating conventional motion planning through optimization of algorithms and hardware to allow safe use of robotic tools in work areas with humans. Luca spoke to our interviewer Kate about Realtime Robotic’s fast motion planning technology, including key aspects, such as perception, algorithms and custom hardware.

 

Luca Colasanto

Luca Colasanto is a Sr. Scientist at Realtime Robotics focusing on AI-based grasping and multi-robot optimization. Luca completed his PhD in Humanoid Robotics at Italian Institute of Technology, focusing on control systems for bipedal walking machines and compliant actuators. During the past ten years, Luca has worked with successful startups to design and prototype new robotics products, and with science and technology leaders, such as Robert Bosch LLC (CA), Institute for Human and Machine Cognition (FL), and École Polytechnique Fédérale de Lausanne (Switzerland), He is the listed inventor in two patent applications and the author of several scientific publications. Luca speaks four languages and is a keen traveler, having visited or lived in more than 20 countries.

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