Institute for Dynamic Systems and ControlOpen OpportunitiesAbstract
Reinforcement learning (RL) has achieved remarkable performance in various domains such as gaming, protein folding, and foundation models. However, efficiently applying RL to real-world applications like go-kart racing and urban driving presents significant challenges due to high-dimensional environments and the lack of structured task decomposition. This thesis proposes addressing these challenges through prioritized rewards and hierarchical task decomposition. By incorporating prioritized experience replay and dynamic reward shaping, the learning process focuses on critical experiences, enhancing efficiency. Hierarchical RL will break down complex tasks into manageable sub-tasks for better strategic planning and execution. The goal is to develop robust and adaptable RL agents capable of high performance in both racing and urban driving scenarios. The student will select a specific application domain, define benchmarks, and potentially conduct real-world testing. The outcomes are expected to contribute significantly to robotics research, with potential publications in top conferences and journals. Pre-requisites include a strong interest in machine learning, RL, optimization, robotics, and proficiency in Python. Prior experience in autonomous vehicles or robotics is a plus. - Automotive Engineering, Intelligent Robotics, Knowledge Representation and Machine Learning
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| Our aim is to create an autonomous racing system capable of swiftly learning optimal racing strategies and navigating tracks more effectively (faster) than traditional methods and human drivers using RL.
- Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| Mobility is typically self-optimized for a particular region to accommodate internal travel needs. However, as soon as one considers multiple, interacting regions (e.g., urban areas interacting with agglomerations, and agglomerations interacting with rural areas), important coordination issues occur, including scheduling mismatches, fleet allocations, and congestion peaks. In short, a mobility system composed of self-optimized mobility systems seems to often operate suboptimally.
In this project, we will investigate the idea of strategic interactions of future mobility stakeholders across heterogeneous regions, such as urban areas, agglomerations, and rural areas, leveraging techniques from network design, optimization, game theory, and policy making. - Automotive Engineering, Information, Computing and Communication Sciences, Mathematical Sciences, Mechanical and Industrial Engineering, Transport Engineering
- Master Thesis, Semester Project
| In this project, we want to explore the application of predictive stability filters for automotive applications. Predictive stability filters allow augmenting human or learning-based controllers such that safety in terms of constraint satisfaction as well as stability of a desired setpoint can be guaranteed. Such algorithms present possible solutions for automotive applications such as, e.g., lane keeping. - Engineering and Technology, Systems Theory and Control
- Master Thesis
| In many autonomous navigation applications, the robot must interact with the environment to learn and complete tasks. Furthermore, these applications are safety-critical, and crashes cannot be afforded. This necessitates the safe learning of the unknown environment in order to achieve the task objective (e.g., detecting a leak or mapping an area). For example, consider an application of safe exploration in a warehouse with a wheeled robot to identify the source of a gas leak. - Mechanical Engineering
- Master Thesis
| A key barrier hindering the swift introduction of autonomous vehicles (AVs) in real-world contexts is the challenge in establishing clear safety benchmarks. Specifically, the issue of systematically assessing both performance and safety remains a significant stumbling block within the industry.
This challenge is mainly twofold: Firstly, how can we identify an ideal scenario set to evaluate the vehicle's performance within a targeted Operational Design Domain (ODD) and what criteria would be useful in amplifying or paring down this set?
Secondly, how do we determine a substantial stopping criteria for the evaluation campaign, and what level of confidence should be attached to the observed performances? - Applied Statistics, Automotive Engineering, Intelligent Robotics, Other
- Master Thesis, Semester Project
| The stochastic diffusion equations ruling the dynamics of particles at the micro- and nano- scale are captured by energy-minimizing dynamics when observed macroscopically, i.e., at a population level. This framework encompasses, for instance, single cells perturbation responses to chemical, genetic or mechanical stimuli, gene expression and cell differentiation.
Recent advances in the theory of optimal transport and optimization in the Wasserstein space have created unprecedented opportunities to tackle these and other problems at scale. This active research area provides an excellent playground for exploring advanced mathematical concepts, deploying sophisticated learning and optimization algorithms, and solving open problems in biology, medicine, and various other fields.
The project can be both theoretical and applied, and can include topics on optimization, optimal transport, deep learning, and biology. The project can be tailored to the preferences and experiences of the student. - Artificial Intelligence and Signal and Image Processing, Biomaterials, Calculus of Variations and Control Theory, Optimisation, Physical Chemistry
- Master Thesis, Semester Project
| Improving and characterizing hardware system to experimentally investigate the interactions of tachycardia and ration therapy. - Biomedical Engineering
- Semester Project
| In this project, we want to explore possible extensions of predictive control barrier functions to the multi-agent setting. Predictive control barrier functions [1] allow certifying safety of a system in terms of constraint satisfaction and provide stability guarantees with respect to the set of safe states in case of initial feasibility. This allows augmenting any human or learning-based controller with closed-loop guarantees through a so-called safety filter [2] which is agnostic to the primary control objective. As current formulations are restricted to single agents, the goal is to investigate how this formulation can be extended for multi-agent applications and how the interactions between the agents can be exploited in order to reduce computational overhead. - Engineering and Technology, Systems Theory and Control
- Master Thesis
| This project focuses on developing autonomous robots for synchronized performances on water. Equipped with kinetic water fountains, RGB lighting, and ultrasonic mist generators, the robots are designed to execute planned choreographies. The system utilizes robotics control, wireless communication, and positioning technologies to coordinate movements, and payload activation, facilitating complex pattern generation and synchronization. The objective is to advance the application of distributed robotic systems in creating structured and cohesive visual displays on water. - Arts, Engineering and Technology, Information, Computing and Communication Sciences
- Bachelor Thesis, Master Thesis, Semester Project
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