Podcast
Wevolver Robots in Depth
Hosted by Wevolver · EN
Robots in Depth (RID) is an interview series with everyone in robotics, from top entrepreneurs, investors and corporate leaders to researchers, political policy makers and domain experts.
The episodes are 30-60 minutes long, and a new episode is published each week.
The format of RID is that of a talk show with one host and one interviewee, both in the same space. The interviews are relaxed and conversational in style and cover every aspect of robotics.
Per Sjöborg hosts Robots in Depth. He is an experienced interviewer in the field of robotics, interviewing robotics experts regularly since he started his podcast in 2010.
Per is well connected in the robotics community and has attended the industry’s major events since 2008.
11episodes
Episodes
Newest firstAll episodes
Building millimeter sized robots w/Professor Julien Bourgeois
Mar 13, 202000:45:57Tap to summarizeJulien Bourgeois talks about self-reconfiguring modular robotics and how he is developing millimeter sized robots called Claytronics.Julien started out as a computer scientist. He was always interested in robotics privately but then had the opportunity to get into micro robots when his lab was merged into the FEMTO-ST Institute. He later worked with Seth Copen Goldstein at Carnegie Mellon on the Claytronics project.He tells us how he works on creating a world built with programmable material that would allow objects to change their form and function automatically by running a program. This will create smart objects that can adapt to the world around them and user preference in a totally new way. One large benefit of programmable matter is that development can happen both in the computer and in the real world with changes transferred between them. A change done in the code would appear in the part made up of programmable matter, but the part can also be changed in the real world and the change would be transferred to the program controlling it. This would create a very flexible, dynamic and highly intuitive design process.The structures based on programmable matter also exhibit many very special characteristics. They can be self-healing if they get damaged, they can dynamically respond to load and be as strong as needed, they can degrade gracefully and predictably and can even indicate that they are overloaded and might fail so that the user can take the appropriate actions.We also learn about a system for sorting very small components he built and how cameras could not be applied.Per and Julien discuss how developing programmable material is hard and that many difficult problems have to be overcome. At the same time, many problems with the current way of doing things will be solved in a fundamentally better way by systems built withprogrammable matter.Julien shows an enlarged mock-up of the small robots that make up programmable matter, catoms, and speaks about how they are designed. Currently he is working on a unit that is one centimeter in diameter and he shows us the very small CPU that goes into that model.There is also an art project in progress, using another version of programmable material building blocks.More about the small CPUs mentioned at https://www.cubeworks.io This episode was recorded at ICML, IJCAI-ECAI, AAMAS in Stockholm, Sweden 2018.This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate.Learn more at Wevolver.comPromote your company in our podcast?If you are interested in sponsoring the podcast, you can contact us at richard@wevolver.com
Transcribe →Rescue robotics & using machine learning to detect gasses w/Achim Lilienthal
Mar 11, 202000:38:52Tap to summarizeAchim talks about rescue robotics and how he is working with integrating sensors that can work and be useful in this challenging application like gas sensors. Achim got in to robotics from working in physics when the team hid did his PhD worked on gas sensors and he saw an opportunity to contribute based on his background in physics.He also talks about a strong personal reason for developing gas sensors as a family member was killed in a gas explosion when he was a kid.We also hear more about the challenges in using commercial senors that are intended for lab use and not for field use mounted on a robot.He talks about how he implements machine learning to detect gasses that was not meant to be in that particular situation.We also get to hear about how you can use different sensors to create a fingerprint of the gases in a situation and how you can use this to great a “heat map” describing what gases are there and at what concentration.This can help in determening the risk of an explosion by sensing gas type, consentration and heat.He also tells us about the smokebot project that aims to oreduce risks for emergensy personel and to use resourcess mor eficently in an timecritical amergency situation.We hear about why it is very hard to deploy robots in many emergensy situations and especilay in fires where there are smoke that blocks most sensrors blinding the robot. One of the few sensors that actually still works are radar and that can offer great asistance to firefighters.This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate.Learn more at Wevolver.comPromote your company in our podcast?If you are interested in sponsoring the podcast, you can contact us at richard@wevolver.com
Transcribe →The impact of robots who start taking decisions like humans do. w/Cristina Andersson
Mar 4, 202000:39:15Tap to summarizeCristina talks about the impact off the ever growing set of tasks that robots can perform and that they can start taking taking decisions like humans do. In 2013 she organized events in Finland during European robotics week and found that many people was very interested but that there was also a big lack of knowledge.She calls of more visions on how we can use robotics to address the challenges society face today. She is especially interested in three areas,Demography, many countries are facing a big change in the numbers of working to non working. She thinks that we need to develop technologies to address the needs of everyone and assure that it is accessible for everyone that needs it.She also sees that robotics have a big role to play in exploring environments that are hostile to humans and that they can make it possible for us to better understand thees environments. This understanding will make it easier to address the most important issues in a efficient way.Her third focus is making education accessible for everyone. When large transformations happen due to the introduction of new technologies it is very important to make it possible for everyone to participate and then education is critical. This is absolutely true for robotics.She also talks about introducing robotics in society in a way that makes it easy for everyone to understand the benefits as this will make the process much easier. When people see the clear benefits in one field or situation they will be much more interested in bringing robotics in to their private or professional lives.She also talks about the Bestick robot that helps people that can not eat by them self, and how profound this is, giving some one back an ability that most of us take for granted.This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate.Learn more at Wevolver.comPromote your company in our podcast?If you are interested in sponsoring the podcast, you can contact us at richard@wevolver.com
Transcribe →Computer vision for field use in agriculture and recycling w/Michael Nielsen
Feb 28, 202000:36:35Tap to summarizeMichael talks about his work in computer vision for field use in agriculture and recycling. He started out in computer vision in the agriculture space doing machine vision and 3D reconstruction of plants. He then moved to the Danish Technological Institute when they expanded their work on machine vision for field use in agriculture.Michael worked with a fusion of sensors like stereo vision, thermography, radar, lidar and high frame rate cameras, merging multiple images for high dynamic range. All this to be able to navigate the tricky situation in a farm field where you need to navigate close to or even in what is grown. Multi-baseline cameras were also used to provide range detection over a wide range of distances.We also learn about how he expanded his work into sorting recycling, a very challenging problem. Here the sensor fusion gives him RGB as well as depth and temperature. Adding a powerful studio flash to the setup allowed him to heat the material being sorted, making it possible to determine the material, depending on how it absorbs the heat from the flash. Michael is also working on adding cameras capable of seeing above the human range of vision to make it easy to specify which materials to pick. We also hear about the problems faced when using time of flight and sheet of light cameras. He then shares some good results using stereo vision, especially combined with blue light random dot projectors.This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate.Learn more at Wevolver.comPromote your company in our podcast?If you are interested in sponsoring the podcast, you can contact us at richard@wevolver.com
Transcribe →Understanding the world around you using game theory w/Nicole Immorlica
Feb 21, 202000:36:46Tap to summarizeNicole talks about game theory and how she feels that it is her way to understand the world around her. Nicole talks about game theory, a way to understand how intelligent agents, humans or machines, interact and optimize their outcome in a particular context.Nicole discusses how this process can be used to create user interactions that are understandable and can be used efficiently.We also hear about how dynamic games apply to robotics and how robots deal with the ever-changing world they act in.Nicole then talks about a trend in market design where large amounts of data about previous behavior is used to redesign the market and optimize it. We also hear about how this is used to understand how people use and interact on social media platforms.She also shares how game theory can be used to explain behavior that is not optimal, for instance in procrastination.This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate.Learn more at Wevolver.com
Transcribe →The social robot revolution w/Gabriel Skantze
Feb 12, 202000:38:28Tap to summarizeGabriel Skantze talks about how he works with human robot communication, and about how the social robot revolution makes it necessary to communicate with humans in a human ways through speech and facial expressions. This is necessary as we expand the number of people that interact with robots as well as the types of interaction.Gabriel gives us more insight into the many challenges of implementing spoken communication for co-bots, where robots and humans work closely together. They need to communicate about the world, the objects in it and how to handle them.We also get to hear how having an embodied system using the Furhat robot head helps the interaction between humans and the system.Having an expressive face like the Furhat adds many improvements to how a system can communicate with people. It also improves the human engagement and understanding of what the system tries to communicate significantly.Gabriel then talks about the how they use AI and machine learning to understand speech. Understanding an individual speaker’s way to speak, thus adapting a robot to its user can improve their communication.As the Furhat system is used out in the field, we get valuable insights from real world situations. One such case is guiding travellers at an airport to improve their experience and make travelling more efficient for everyone.This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate.Learn more at Wevolver.com
Transcribe →Learnings from working + 20 years in robotics as an entrepreneur, teacher, lecturer, and researcher w/Lars Dalgaard
Feb 7, 202000:38:11Tap to summarizeLars Dalgaard shares his experiences developing robots in many different contexts.We hear about Lars’ early work with large mobile robotics in a commercial nursery garden handling the transfer of plants from the greenhouse to the field.He also speaks about the Hydra project a self re-configuring modular robotics project that developed several different modular robotics systems including the Atron system.Lars felt that there was a problem with the process used to introduce robotics and automation into society. Commercialization was hard and unreliable mostly because there was no focus on designing a complete system. This lead to an industrial PhD done at the Danish Technological Institute (DTI) focusing on a system level design approach.He then talks about his work at DTI that focuses on transferring research, knowledge and research results from academia into companies and the Danish society in general.One project Lars has been working on is augmenting mobile platforms so that they can handle tasks as they move around in a production facility. This also aims to make it easier to program the mobile platforms and any systems added to them.Lars thinks that looking at the bigger picture and bringing multiple partners and end users into projects, and doing so early, can bring big benefits to a project.This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate.Learn more at Wevolver.com
Transcribe →Mimicking human decision making with algorithms w/Adjunct Professor Harri Ketamo
Feb 5, 202000:33:00Tap to summarizeHarri talks about AI and how he aims to mimic human decision making with algorithms.Harri has done a lot of AI for computer games to create opponents that are entertaining to play against. It is easy to develop a very bad or a very good opponent, but designing an opponent that behaves like a human, is entertaining to play against and that you can beat is quite hard. He talks about how AI in computer games is a very important story telling tool and an important part of making a game entertaining to play.This work led him into other parts of the AI field. Harri thinks that we sometimes have a problem separating what is real from what is the type of story telling he knows from gaming AI. He calls for critical analysis of AI and says that data has to be used to verify AI decisions and results.We also hear about the current use of AI in among other things sports games, where the challenge is to make a believable computer copy of a real-world player to make the games feel more real.We then get to hear about his current work in developing AI systems that create mind-maps from texts to make the computer able to determine the context. By doing this we can derive better sentiment and meaning. This AI system is trained using a large amount of reliable data from scientific papers and CNN.One application of this that Harri is working on, is in the labor market matching needs for talent with available people. This will help companies find staff and people find job opportunities.This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate.Learn more at Wevolver.comPromote your company in our podcast?If you are interested in sponsoring the podcast, you can contact us at richard@wevolver.com
Transcribe →Building Artificial Intelligence systems that can interact with humans w/VP of A.I Christian Guttmann
Jan 31, 202000:24:01Tap to summarizeChristian talks about AI and wanting to understand intelligence enough to recreate it.Christian discusses building systems that can interact with humans beyond regular computer interfaces.He started working with computers early, too early for advanced AI. He studied broadly, including philosophy, ethics as well as synthetic and biological neural networks.Christian has found a lot of inspiration in the old papers by Alan Turing and others. He sometimes envies them their opportunity to think big, as they did when they founded the areas of computer science and artificial intelligence.Christian has be focusing on AI in healthcare and has recently started to communicate the opportunities and challenges in artificial intelligence to the general public. This is something that the host Per Sjöborg is also very passionate about.We also get to hear about the Nordic AI institute (https://www.nordicaiinstitute.com) and the work it does to inform all parts of society about AI. Anyone interested in AI is welcome to reach out if they have questions or if they have knowledge to share.Then we hear how Christian is working with Tieto (https://www.tieto.com) on integrating artificial intelligence in their customers daily operations.This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate.Learn more at Wevolver.com Promote your company in our podcast?If you are interested in sponsoring the podcast, you can contact us at richard@wevolver.com
Transcribe →Real world challenges with Artificial Intelligence w/Associate Professor Federico Pecora
Jan 30, 202000:37:17Tap to summarizeFederico talks about AI and how the computer decides what to do and when to do it.Federico finds many interesting research problems combining AI and robotics. Being out in the real world challenges the AI algorithms and makes real physical tasks possible.Federico talks about working on AI and service robotics. In this area he has worked on planning, especially focusing on why a particular goal is the one that the robot should work on. To make robots as useful and user friendly as possible, he works on inferring the goal from the robot’s environment so that the user does not have to tell the robot everything.Federico has also worked with AI robotics planning in industry to optimize results. Managing the relative importance of tasks is another challenging area there. In this context, he works on automating not only a single robot for its goal, but an entire fleet of robots fortheir collective goal. We get to hear about how these techniques are being used in warehouse operations, in mines and in agriculture.Federico discusses how important the co-operation between roboticists and AI specialists is. It can sometimes be challenging, but when it works it yields very interesting questions and results.This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate.Learn more at Wevolver.comPromote your company in our podcast?If you are interested in sponsoring the podcast, you can contact us at richard@wevolver.com
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