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Autonomous Drone Navigation via Learning from YouTube Videos
Inspired by how humans learn, this project aims to explore the possibility of learning flight patterns, obstacle avoidance, and navigation strategies by simply watching drone flight videos available on YouTube.
The evolving landscape of large vision and language models, paired with the untapped availability of unlabeled internet data, presents new exciting opportunities for training robotic policies. Inspired by how humans learn, this project aims to explore the possibility of learning flight patterns, obstacle avoidance, and navigation strategies by simply watching drone flight videos available on YouTube. State-of-the-art methods for processing and encoding videos, as well as unsupervised training techniques, will be evaluated and designed during the project. Applicants should have a strong background in machine learning, computer vision, and proficiency in Python programming. Familiarity with deep learning frameworks such as PyTorch is desirable.
The evolving landscape of large vision and language models, paired with the untapped availability of unlabeled internet data, presents new exciting opportunities for training robotic policies. Inspired by how humans learn, this project aims to explore the possibility of learning flight patterns, obstacle avoidance, and navigation strategies by simply watching drone flight videos available on YouTube. State-of-the-art methods for processing and encoding videos, as well as unsupervised training techniques, will be evaluated and designed during the project. Applicants should have a strong background in machine learning, computer vision, and proficiency in Python programming. Familiarity with deep learning frameworks such as PyTorch is desirable.
Investigate the feasibility and effectiveness of using large vision models along with self-supervised learning techniques to teach drones to navigate autonomously by analyzing YouTube videos. Develop a prototype system capable of learning from online videos and demonstrate its effectiveness in simulated and real-world environments.
Investigate the feasibility and effectiveness of using large vision models along with self-supervised learning techniques to teach drones to navigate autonomously by analyzing YouTube videos. Develop a prototype system capable of learning from online videos and demonstrate its effectiveness in simulated and real-world environments.
Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Marco Cannici (cannici AT ifi DOT uzh DOT ch) and Angel Romero (roagui AT ifi DOT uzh DOT ch)
Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Marco Cannici (cannici AT ifi DOT uzh DOT ch) and Angel Romero (roagui AT ifi DOT uzh DOT ch)