arXiv:1809.08881
공식 코드 · Papers with Code
Vision-based Control of a Quadrotor in User Proximity: Mediated vs End-to-End Learning Approaches
원문 초록
We consider the task of controlling a quadrotor to hover in front of a freely moving user, using input data from an onboard camera. On this specific task we compare two widespread learning paradigms: a mediated approach, which learns an high-level state from the input and then uses it for deriving control signals; and an end-to-end approach, which skips high-level state estimation altogether. We show that despite their fundamental difference, both approaches yield equivalent performance on this task. We finally qualitatively analyze the behavior of a quadrotor implementing such approaches.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose Estimation
Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation
PoseBench3D: A Cross-Dataset Analysis Framework for 3D Human Pose Estimation
Benchmarking Spatiotemporal Reasoning in LLMs and Reasoning Models: Capabilities and Challenges
UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units
토론
이 논문과 연결된 포럼 글이 아직 없습니다.