Research FrazzoliOpen 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
| 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
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