Keynote Speakers


Prof. Richard Voyles, Purdue University, USA

IEEE Fellow, Founding Director of the Purdue Robotics Accelerator

Biography: Dr. Richard Voyles, the Daniel C. Lewis Professor of the Polytechnic, has been a researcher, deployer, and advocate for robotics and the internet of things most of his academic and professional career. He is currently professor of robotics in the Polytechnic Institute at Purdue University as well as the founding director of the Purdue Robotics Accelerator and was named a University Faculty Scholar in 2014, Indiana Manufacturing Institute Fellow in 2019, and IEEE Fellow in 2021. He has been in academe for 25 years, served the US federal government for 5 years, and spent 6 years in industry. At the White House Office of Science and Technology Policy, he served as Assistant Director for Robotics and Cyber-Physical Systems, during which he advocated for increased funding for robotics and IoT research and pushed for "filling the gaps" in the educational continuum "from HS to MS," including Engineering Technology. Prior to this, he was founding Program Director at the National Science Foundation for the National Robotics Initiative, was one of the founding Program Directors of the Innovation Corps program, and a Program Director in Cyber-Physical Systems. He leads the Collaborative Robotics Lab and is Site Director of the NSF Center for Robotics and Sensors for Human Well-Being (RoSe-HUB) at Purdue. Prof. Voyles' educational background includes the three pillars of robotics -- electrical engineering, mechanical engineering and computer science -- having received the B.S.E.E. from Purdue University in 1983, the M.S.M.S.E. from Mechanical Engineering at Stanford University in 1989, and the Ph.D. in Robotics from the School of Computer Science at Carnegie Mellon University in 1997. Prof. Voyles' prior appointments have included engineering positions in industry at Dart Controls, IBM, Integrated Systems, and Avanti Optics and tenured positions in Computer Science at the University of Minnesota and Electrical and Computer Engineering at the University of Denver. Dr. Voyles' research interests are in the areas of small, resource-constrained robots and robot teams for urban search and rescue and surveillance, new generations of co-robots for intelligent, human-assistive tasks, such as nuclear clean-up, and intelligent meta-materials that combine sensing, computation and structure. He has founded or served on the boards of several start-ups and also served several non-profit groups with a focus on STEM education.

Speech Title: Directions for UAS in Fluidic Environments


Prof. Jan Peters, Darmstadt University of Technology, Germany

IEEE Fellow, ELLIS Fellow, Full professor (W3) for Intelligent Autonomous Systems

Biography: Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems - Early Career Spotlight, the INNS Young Investigator Award, and the IEEE Robotics & Automation Society's Early Career Award as well as numerous best paper awards. In 2015, he received an ERC Starting Grant and in 2019, he was appointed as an IEEE Fellow and in 2020 an ELLIS fellow.

Despite being a faculty member at TU Darmstadt only since 2011, Jan Peters has already nurtured a series of outstanding young researchers into successful careers. These include new faculty members at leading universities in the USA, Japan, Germany, Finland and Holland, postdoctoral scholars at top computer science departments (including MIT, CMU, and Berkeley) and young leaders at top AI companies (including Amazon, Google and Facebook).

Jan Peters has studied Computer Science, Electrical, Mechanical and Control Engineering at TU Munich and FernUni Hagen in Germany, at the National University of Singapore (NUS) and the University of Southern California (USC). He has received four Master's degrees in these disciplines as well as a Computer Science PhD from USC. Jan Peters has performed research in Germany at DLR, TU Munich and the Max Planck Institute for Biological Cybernetics (in addition to the institutions above), in Japan at the Advanced Telecommunication Research Center (ATR), at USC and at both NUS and Siemens Advanced Engineering in Singapore. He has led research groups on Machine Learning for Robotics at the Max Planck Institutes for Biological Cybernetics (2007-2010) and Intelligent Systems (2010-2021).

Speech Title: Robot Reinforcement Learning

Abstract: Autonomous robots that can assist humans in situations of daily life have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this talk, we investigate a general framework suitable for learning motor skills in robotics which is based on the principles behind many analytical robotics approaches. It involves generating a representation of motor skills by parameterized motor primitive policies acting as building blocks of movement generation, and a learned task execution module that transforms these movements into motor commands. We discuss learning on three different levels of abstraction, i.e., learning for accurate control is needed to execute, learning of motor primitives is needed to acquire simple movements, and learning of the task-dependent „hyperparameters“ of these motor primitives allows learning complex tasks. We discuss task-appropriate learning approaches for imitation learning, model learning and reinforcement learning for robots with many degrees of freedom. Empirical evaluations on a several robot systems illustrate the effectiveness and applicability to learning control on an anthropomorphic robot arm. These robot motor skills range from toy examples (e.g., paddling a ball, ball-in-a-cup) to playing robot table tennis against a human being and manipulation of various objects.