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Drone Racing End-to-end policy learning: from features to commands
This project focuses on using RL to learn quadrotor policies to fly at high speeds in complex tracks, directly from features.
Keywords: Robotics, Autonomous Systems, Reinforcement Learning, Quadcopter, Drone Racing
This project focuses on using RL to learn quadrotor policies to fly at high speeds in complex tracks, directly from features.
This project focuses on using RL to learn quadrotor policies to fly at high speeds in complex tracks, directly from features.
The primary objective of this thesis is to use the current RL pipeline to explore the possibility of learning directly from a feature map, instead of other representations. The project involves several key stages: collecting a feature map of a real-world environment, transferring these real-world features into a simulator, using RL to train the drone in this simulated environment, and finally deploying this learning in the real world with real-time feature detection.
Applicant Requirements:
- Proficiency in machine learning, specifically in Reinforcement Learning.
- Experience in programming with Python and C++.
- Knowledge in simulation software and real-time data processing.
- Understanding of drone dynamics and control systems.
- Background in signal processing and non-linear dynamic systems.
- Additional experience in image processing and embedded systems is advantageous.
The primary objective of this thesis is to use the current RL pipeline to explore the possibility of learning directly from a feature map, instead of other representations. The project involves several key stages: collecting a feature map of a real-world environment, transferring these real-world features into a simulator, using RL to train the drone in this simulated environment, and finally deploying this learning in the real world with real-time feature detection. Applicant Requirements: - Proficiency in machine learning, specifically in Reinforcement Learning. - Experience in programming with Python and C++. - Knowledge in simulation software and real-time data processing. - Understanding of drone dynamics and control systems. - Background in signal processing and non-linear dynamic systems. - Additional experience in image processing and embedded systems is advantageous.
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Angel Romero (roagui AT ifi DOT uzh DOT ch), Giovanni Cioffi (cioffi AT ifi DOT uzh DOT ch), Ismail Geles (geles AT ifi DOT uzh DOT ch) and Jiaxu Xing (xing AT ifi DOT uzh DOT ch)
Contact Details: Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Angel Romero (roagui AT ifi DOT uzh DOT ch), Giovanni Cioffi (cioffi AT ifi DOT uzh DOT ch), Ismail Geles (geles AT ifi DOT uzh DOT ch) and Jiaxu Xing (xing AT ifi DOT uzh DOT ch)