E2E-Fly supplies an end-to-end training, validation, and deployment stack that lets researchers train differentiable-physics-based policies for six quadrotor tasks and transfer them directly to two physical platforms.
A comparative study of nonlinear mpc and differential-flatness-based control for quadrotor agile flight,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Self-supervised residual learning from trajectory data forms a hybrid dynamics model that enables trajectory optimization to produce aggressive yet precisely trackable motions for quadrotors.
citing papers explorer
-
E2E-Fly: An Integrated Training-to-Deployment System for End-to-End Quadrotor Autonomy
E2E-Fly supplies an end-to-end training, validation, and deployment stack that lets researchers train differentiable-physics-based policies for six quadrotor tasks and transfer them directly to two physical platforms.
-
Optimizing Control-Friendly Trajectories with Self-Supervised Residual Learning
Self-supervised residual learning from trajectory data forms a hybrid dynamics model that enables trajectory optimization to produce aggressive yet precisely trackable motions for quadrotors.