Motor Control & Learning, Robotics, Machine Learning, Biomimetic Systems. Room E314, Robert-Piloty-Gebaeude S2|02 Jan Peters graduated from the University of Hagen in 2000 with a Diplom-Informatiker (German M. in Computer Science) with a focus on artificial intelligence and from Munich University of Technology (TU Muenchen) in 2001 with a Diplom-Ingenieur Elektrotechnik (German M. in Electrical Engineering), majoring in automation & control.
Curriculum Vitae Short Bio Publications Google Scholar DBLP ORCID Mail. In 2000-2001, he spent two semesters as visiting student at National University of Singapore.
One of our early effort towards this was M2V2, where several organizations and many individuals had a significant influence on the work and/or are currently collaborating with us. Capture Point: A Step toward Humanoid Push Recovery.
The x_dot_0 in the RHS of Equations 48 and 51 should be squared.
A major advantage of the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances.
We demonstrate our method on a simulation of a plane flying through a two dimensional forest of polygonal trees with parametric uncertainty and disturbances in the form of a bounded ”cross-wind”.I'm working on my Ph D in Computer Science at MIT with Russ Tedrake in the Robot Locomotion Group within CSAIL.Prior to starting the Ph D I worked on the MIT Darpa Robotics Challenge Team doing planning and controls for the Atlas robot.“Sensors like lidar are too heavy to put on small aircraft, and creating maps of the environment in advance isn’t practical.If we want drones that can fly quickly and navigate in the real world, we need better, faster algorithms.”That’s where Barry’s drone, developed as part of his thesis with MIT professor Russ Tedrake, comes into play.We leverage sums-of-squares programming in order to efficiently compute funnels which take into account bounded disturbances and uncertainty.The resulting motion plans at runtime while ensuring the safety of the robot.Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt and at the same time an adjunct senior research scientist at the Max-Planck Institute for Intelligent Systems, where he heads the interdepartmental Robot Learning Group between the departments of Empirical Inference and Autonomous Motion.Jan Peters has received a few awards, most notably, he has received the Dick Volz Best US Ph D Thesis Runner Up Award, the Robotics: Science & Systems - Early Career Spotlight, the IEEE Robotics & Automation Society's Early Career Award, and the International Neural Networks Society's Young Investigator Award.If we have open positions, they will be listed at here.Scholarship applications (including DAAD WISE, CSC or other) and internships will also only be considered if send through our application website.