Soft Evolutionary Robotics: Adapting morphology and control to the world around us
Andre Rosendo, ShanghaiTech University, China
ATLASCAR: a sample of the quests and concerns for Autonomous Cars
Vitor Santos, Universidade de Aveiro, Portugal
Deep Learning for Robot Navigation and Perception
Wolfram Burgard, University of Freiburg, Germany
Robotic Dressing Assistants: Research, Ethics and Fiction
Carme Torras, , Spain
Soft Evolutionary Robotics: Adapting morphology and control to the world around us
Andre Rosendo
ShanghaiTech University
China
Brief Bio
Dr. Andre Rosendo received his Bachelor at Bahia Federal University, and Master and Ph.D. degrees from Hokkaido University and Osaka University, in 2011 and 2014, respectively. In 2014 he was hired as a Specially Appointed Assistant Professor by the Adaptive Robotics Laboratory in Osaka University, and in 2015 he started his role as a Research Associate at the Bio-inspired Robotics Laboratory at the University of Cambridge. His research interests are related to evolutionary robotic systems and to robotic locomotion by means of hopping, walking and running. When studying the morphological evolution of robots, he seeks to understand how machines can create other machines, the influence of evolution and development on the performance of robots and the results of "human-free" robotic design. From a locomotion perspective, he studies the contribution of soft actuators, such as muscles and springs, to a higher stability and energy efficiency, and the positive influence of such "bio-inspired" designs to locomotion.
Abstract
Biological forms started primitively and, eventually, some of these forms evolved and reached intelligence. Although current artificial intelligence (AI) explores learning techniques to create thinking machines, is “thinking” really a condition for “existence”? Instead of creating intelligent computer programs to later interact with the physical world, my research will start with physical interactions and create intelligence from there, as achieved by humans. I will present our latest results with Mother Robot, a robot capable of creating children robots, which are smaller robots with the capacity of growing their bodies according to cues from the interaction with the environment. Additionally, I will talk about robots capable of simultaneously altering their morphology and control and compare this method with other design ramifications, such as solely altering control parameters. The future of robotics remains uncertain, but the capacity of adapting their self-design and altering their morphology to fulfill tasks more effectively can be a driving force for future Evolutionary Robotics.
ATLASCAR: a sample of the quests and concerns for Autonomous Cars
Vitor Santos
Universidade de Aveiro
Portugal
Brief Bio
Dr. Vitor Santos obtained a 5-year degree in Electronics Engineering and Telecommunications in 1989, at the University of Aveiro, Portugal, where he later obtained a PhD in Electrical Engineering in 1995. He was awarded fellowships to pursue research in mobile robotics during 1990-1994 at the Joint Research Center, Italy. He is currently Associate Professor at the University of Aveiro and lectures courses related to advanced perception and robotics, and has carried out research activity on mobile robotics, advanced perception and humanoid robotics. At the University of Aveiro he has coordinated the ATLAS project for mobile robot competition that achieved 6 first prizes in the annual Autonomous Driving competition and has coordinated the development of ATLASCAR the first real car with autonomous navigation capabilities in Portugal. He is one of the founders of the Portuguese Robotics Open in 2001 where he maintained active participation for more than 12 years. He is also co-founder of the Portuguese Society of Robotics, and participated several times in its management since its foundation in 2006. His current interests extend to hybrid humanoid robotics and the application of techniques from perception and mobile robotics to autonomy and safety in ADAS contexts.
Abstract
Autonomous cars have become an irreversible trend in the modern world society from several points of view, and will be one of the most relevant topics in mobility and transportation in the near and long term future. However, despite the many solutions and prototypes that have been rising in a growing set of countries in the last years, no definitive and universally usable solution has been made available by automotive companies or research laboratories. The issues of vehicle control are perhaps the best solved due to a long term research in engineering and actuators technology, but what still remains as a challenge is the reliable awareness of the vehicle in what concerns the dynamic and complex environment where it is embedded, namely, in urban scenarios. This concern is related mainly to robust and reliable perception and data processing.
ATLASCAR is a project born in 2010 where many engineering issues had to be solved, but that represent only by themselves too little as far as pushing forward the state-of-art is concerned. What possibly emerged from this project as contributions useful to other authors and researchers are the options in the system architecture, but mainly the study of a set of problems less frequently addressed, but that can make a difference in the long term intelligent vehicles. Some simpler examples are proprioceptive issues such as the automatic calibration of multi-modal sensors or the measurement with high precision of the vehicle orientation on the road plane, up to concerns of pedestrian posture detection to predict their behaviour, or how to automatically merge into the traffic flow with other moving vehicles. Pushing the challenge even further, assess the driving typology or characteristics of the driver navigation behaviour may point to a new level of driving assistance. Actually, although autonomous driving is sought and desired by many, it is expectable that future cars will allow at least two control modes: automatic or manual with advanced assistance systems. So, this ability to assess and monitor the driver behaviour and driving typology may open the way to some sort of predictive and adaptive driver assistance systems, which is so, or even more important than the autonomous driving capabilities of the car.
Deep Learning for Robot Navigation and Perception
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Brief Bio
Wolfram Burgard is a professor for computer science at the University of Freiburg and head of the research lab for Autonomous Intelligent Systems. His areas of interest lie in artificial intelligence and mobile robots. His research mainly focuses on the development of robust and adaptive techniques for state estimation and control. Over the past years Wolfram Burgard and his group have developed a series of innovative probabilistic techniques for robot navigation and control. They cover different aspects such as localization, map-building, SLAM, path-planning, exploration, and several other aspects. Wolfram Burgard coauthored two books and more than 300 scientific papers. In 2009, Wolfram Burgard received the Gottfried Wilhelm Leibniz Prize, the most prestigious German research award. In 2010, Wolfram Burgard received an Advanced Grant of the European Research Council. Since 2012, Wolfram Burgard is the coordinator of the Cluster of Excellence BrainLinks-BrainTools funded by the German Research Foundation. Wolfram Burgard is Fellow of the ECCAI, the AAAI, and the IEEE.
Abstract
Autonomous robots are faced with a series of learning problems to optimize their behavior. In this presentation I will describe recent approaches developed in my group based on deep learning architectures for different perception problems including object recognition and segmentation and using RGB(-D) images. In addition, I will present a terrain classification approaches that utilize sound and vision. For all approaches I will describe extensive experiments quantifying in which way the corresponding approaches extend the state of the art.
Robotic Dressing Assistants: Research, Ethics and Fiction
Brief Bio
Carme Torras (www.iri.upc.edu/people/torras) is Research Professor at the Spanish Scientific Research Council (CSIC), and Head of the Perception and Manipulation group at the Robotics Institute in Barcelona. She holds M.Sc. degrees in Mathematics and Computer Science from the University of Barcelona and the University of Massachusetts, Amherst, respectively, and a Ph.D. degree in Computer Science from the Technical University of Catalonia (UPC). Prof. Torras has published five books and about three hundred papers in the areas of robot kinematics, computer vision, geometric reasoning, machine learning and manipulation planning. She has led 12 European projects and supervised 18 PhD theses on these topics, and she is currently Editor of the IEEE Transactions on Robotics. She was Associate Vice-President for Publications of the IEEE Robotics and Automation Society (RAS), and has been elected to serve in the Administrative Committee of IEEE RAS in the period 2016-2018.
Prof. Torras has participated in many activities to promote Ethics in Robotics: she has delivered talks at local, national and international venues (e.g., at ICRA-13’s forum “Robotics meets the Humanities”), she has written essays on science fiction and ethics, and she is currently developing some pedagogical materials to teach Roboethics based on her novel The Sentimental Mutation. Prof. Torras was awarded the Narcís Monturiol Medal of the Generalitat de Catalunya in 2000, and became EurAI Fellow in 2007, member of Academia Europaea in 2010, and Member of the Royal Academy of Sciences and Arts of Barcelona in 2013. She has been awarded an Advanced Grant with a project entitled “Clothilde - Cloth manipulation learning from demonstrations” to start in 2017.
Abstract
The versatile manipulation of clothing items by robots would considerably enlarge their range of application, from textile manufacturing to online shopping, housekeeping, hospital logistics, and providing dressing assistance to elderly and disabled people. To carry out these tasks in unprepared human environments, robots need to be easily instructed by non-experts, intrinsically safe to people, capable of communicating and working collaboratively, and highly adaptable to changing situations and new goals. A quick overview of the research challenges arising in this context will be illustrated through projects being developed at the Perception and Manipulation Lab of IRI (CSIC-UPC).
Robotic assistants pose also fundamental ethical questions, many practical ones stemming from autonomous robot decision making conflicting with human freedom and dignity. Philosophy, psychology and law are shedding principled light on these conflicts, while science fiction permits freely speculating about possible future scenarios, thus encouraging debate on the pros and cons to try to guide techno-scientific research in the most desirable direction.