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.
Sim-to-real transfer of robotic control with dynamics randomization,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2representative citing papers
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.
- Zero-Shot MARL Benchmark in the Cyber-Physical Mobility Lab