Chapter
Advancements in Machine Learning and Reinforcement Learning to Optimize Flight Efficiency
27:42 - 32:04 (04:21)
The field of machine learning and reinforcement learning, particularly with the neural network variant of deep reinforcement learning, is making strides in optimizing flight efficiency for flying robots. While success in flying robots has not completely relied on machine learning, advancements in perception through computer vision and the idea of learning have greatly contributed to flying through constrained spaces.
Clips
This podcast discusses the progress and excitement in the field of machine learning and deep reinforcement learning, as well as the success of flying robots that did not rely heavily on this technology, showcasing the simple idea of being able to fly through a constrained space.
27:42 - 30:52 (03:10)
Summary
This podcast discusses the progress and excitement in the field of machine learning and deep reinforcement learning, as well as the success of flying robots that did not rely heavily on this technology, showcasing the simple idea of being able to fly through a constrained space.
ChapterAdvancements in Machine Learning and Reinforcement Learning to Optimize Flight Efficiency
EpisodeVijay Kumar: Flying Robots
PodcastLex Fridman Podcast
Microclimates such as ground effect, wall effect, and ceiling effect can significantly affect the performance of UAVs as the models rely on the assumption that the blades are rigid, but in reality, they bend and flap.
30:53 - 32:04 (01:10)
Summary
Microclimates such as ground effect, wall effect, and ceiling effect can significantly affect the performance of UAVs as the models rely on the assumption that the blades are rigid, but in reality, they bend and flap. These effects become even more significant when flying at high speeds or around obstacles.