Research Zeilinger

Open Opportunities

Efficient Sampling-based GP-MPC for autonomous robots ETH Zurich Research Zeilinger Most control methods operate under the assumption of a known model. However, in practice, knowing the exact dynamics model a priori is unrealistic. A common approach is to model the unknown dynamics using Gaussian Processes (GPs) which can characterize uncertainty and formulate a Model Predictive Control (MPC) type problem. However, it is difficult to exactly utilize this uncertainty characterization in predictive control. In a recent approach [1], we proposed a sampling-based robust GP-MPC formulation for accurate uncertainty propagation by sampling continuous functions. In contrast, in the proposed project, you will implement an approximation method for sampling continuous functions using a finite number of basis functions [2] and solve the MPC problem jointly with the sampled dynamics. You will analyze the trade-offs between performance, approximation accuracy, and computational cost for this method. Engineering and Technology Master Thesis, Semester Project

Robust predictive control for safe and optimal control of nonlinear uncertain systems ETH Zurich Research Zeilinger This project aims to improve the design of predictive controllers that robustly ensure safe operation for a large class of uncertain nonlinear systems. Dynamical Systems, Systems Theory and Control ETH Zurich (ETHZ), Master Thesis

Online Learning of Dynamic Control for Soft Manipulators ETH Zurich Research Zeilinger This project aims to develop an online learning framework for achieving precise position control of a soft robotic arm while adapting to time-varying system dynamics. Engineering and Technology Master Thesis

Digital Twin for Spot's Home ETH Zurich Computer Vision and Geometry Group Other organizations: Research Zeilinger, Robotic Systems Lab MOTIVATION ⇾ Creating a digital twin of the robot's environment is crucial for several reasons: 1. Simulate Different Robots: Test various robots in a virtual environment, saving time and resources. 2. Accurate Evaluation: Precisely assess robot interactions and performance. 3. Enhanced Flexibility: Easily modify scenarios to develop robust systems. 4. Cost Efficiency: Reduce costs by identifying issues in virtual simulations. 5. Scalability: Replicate multiple environments for comprehensive testing. PROPOSAL We propose to create a digital twin of our Semantic environment, designed in your preferred graphics Platform to be able to simulate Reinforcement Learning agents in the digital environment, to create a unified evaluation platform for robotic tasks. Artificial Intelligence and Signal and Image Processing Master Thesis, Semester Project