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Swarm navigation at 20 m/s with no communication, no state estimation, & on a $21 computer? This paper combines deep learning + differentiable sim for zero-shot sim-to-real flight. It is an underrated breakthrough. Paper: #naturemachineintelligence

17,515 次观看 • 1 年前 •via X (Twitter)

5 条评论

Chuck Petras 的头像
Chuck Petras1 年前

@GrandpaRoy2 @johnrobb @BrianRoemmele

kache 的头像
kache1 年前

thank you for sharing

Irene 的头像
Irene1 年前

Does this mean that future drones will be smaller and lighter and less expensive?

MangoMagic™️ 的头像
MangoMagic™️1 年前

Remarkable. Just wait until the drones start taking selfies. #FlyingUnderTheRadar #BudgetBillionaires #DeepLearningDisco

Zanir Habib 的头像
Zanir Habib1 年前

Hmmm....it seems the best methods are going to be those that pipe in the most data about physics/the environment? Was reading that genetic algos are also good at these types of problems

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We are thrilled to share our breakthrough research on "Agile Flight from Pixels without State Estimation," to be presented and live-demonstrated at #RSS2024 next week! You heard well: no state estimation means no explicit visual localization, no SLAM, no VIO, and no IMU! Paper: Video (Narrated): Last year, we demonstrated that #ReinforcementLearning (RL) policies could outperform world-champion drone-racing pilots using the same quadrotor hardware; however, unlike human pilots, these policies continuously estimated an explicit state from known gate positions, the camera feed, and inertial measurements (IMU). In this new work, we tackle the challenge of learning vision-based drone racing using an end-to-end reinforcement learning approach that eliminates the need for IMU data or explicit state estimation. Like professional pilots, we go directly from images to control commands. The training is facilitated by an asymmetric actor-critic with access to privileged information. To overcome the computational complexity during image-based RL training, we use an appropriate sensor representation, which can be efficiently simulated during training without rendering images. We achieve agile flight at speeds up to 40 km/h with accelerations up to 2 g's. Although our demonstration focuses on drone racing, we believe that our method has an impact beyond drone racing and can serve as a foundation for future research into real-world applications in structured environments. Besides the paper presentation, we will also give a live demo next Tuesday and Wednesday between and hrs at TU Delft: Reference: Ismail Geles*, Leonard Bauersfeld*, Angel Romero, Jiaxu Xing, Davide Scaramuzza "Demonstrating Agile Flight from Pixels without State Estimation" Robotics: Science and Systems (RSS), 2024. Kudos to Ismail Geles Leonard Bauersfeld Ángel Romero Jiaxu Xing! University of Zurich UZH Science UZH Space Hub Aerial Core AUTOASSESS European Research Council (ERC)

Davide Scaramuzza

28,002 次观看 • 2 年前