Recent advancements in machine learning have highlighted the potential of Long Sequence Modeling as a powerful approach for handling complex temporal dependencies, positioning it as a compelling alternative to traditional Transformer-based models. In the context of drone racing, where split-second decision-making and precise control are of greatest importance, Long Sequence Modeling can offer significant improvements. These models are adept at capturing intricate state dynamics and handling continuous-time parameters, providing the flexibility to adapt to varying time steps essential for high-speed navigation and obstacle avoidance. This project aims to bridge this gap by investigating the application of Long Sequence Modeling techniques in RL to develop advanced autonomous drone racing systems. The ultimate goal is to improve autonomous drones' performance, reliability, and adaptability in competitive racing scenarios.
Recent advancements in machine learning have highlighted the potential of Long Sequence Modeling as a powerful approach for handling complex temporal dependencies, positioning it as a compelling alternative to traditional Transformer-based models. In the context of drone racing, where split-second decision-making and precise control are of greatest importance, Long Sequence Modeling can offer significant improvements. These models are adept at capturing intricate state dynamics and handling continuous-time parameters, providing the flexibility to adapt to varying time steps essential for high-speed navigation and obstacle avoidance. This project aims to bridge this gap by investigating the application of Long Sequence Modeling techniques in RL to develop advanced autonomous drone racing systems. The ultimate goal is to improve autonomous drones' performance, reliability, and adaptability in competitive racing scenarios.
Develop a Reinforcement Learning framework based on Long Sequence Modeling tailored for drone racing. Simulate the framework to evaluate its performance in controlled environments. Conduct a comprehensive analysis of the framework’s effectiveness in handling long sequences and dynamic racing scenarios. Ideally, the optimized model should be deployed in real-world drone racing settings to validate its practical applicability and performance.
Develop a Reinforcement Learning framework based on Long Sequence Modeling tailored for drone racing. Simulate the framework to evaluate its performance in controlled environments. Conduct a comprehensive analysis of the framework’s effectiveness in handling long sequences and dynamic racing scenarios. Ideally, the optimized model should be deployed in real-world drone racing settings to validate its practical applicability and performance.
Interested candidates should send their CV, transcripts (bachelor and master) to Nikola Zubic (zubic@ifi.uzh.ch), Angel Romero Aguilar (roagui@ifi.uzh.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
Interested candidates should send their CV, transcripts (bachelor and master) to Nikola Zubic (zubic@ifi.uzh.ch), Angel Romero Aguilar (roagui@ifi.uzh.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
