# Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight

arXiv: https://arxiv.org/abs/1902.03701 · HTML: https://mlsanity.com/paper/1902.03701/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2019.

## Built on by (most cited first, 28 of 28)

- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments](https://mlsanity.com/paper/2108.03332/) (2021)
- [Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability](https://mlsanity.com/paper/2107.06277/) (2021)
- [How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence](https://mlsanity.com/paper/2305.01263/) (2023)
- [Aerial Vision-and-Dialog Navigation](https://mlsanity.com/paper/2205.12219/) (2022)
- [See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned Aerial Navigation](https://mlsanity.com/paper/2509.22653/) (2025)
- [AeroDuo: Aerial Duo for UAV-based Vision and Language Navigation](https://mlsanity.com/paper/2508.15232/) (2025)
- [Sim and Real: Better Together](https://mlsanity.com/paper/2110.00445/) (2021)
- [Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning](https://mlsanity.com/paper/2209.06336/) (2022)
- [Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics](https://mlsanity.com/paper/2502.07380/) (2025)
- [Depth Transfer: Learning to See Like a Simulator for Real-World Drone Navigation](https://mlsanity.com/paper/2505.12428/) (2025)
- [Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm](https://mlsanity.com/paper/2102.00168/) (2021)
- [Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach](https://mlsanity.com/paper/2302.13423/) (2023)
- [Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning](https://mlsanity.com/paper/2303.02234/) (2023)
- [A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones](https://mlsanity.com/paper/2307.09880/) (2023)
- [A Twin Delayed Deep Deterministic Policy Gradient Algorithm for Autonomous Ground Vehicle Navigation via Digital Twin Perception Awareness](https://mlsanity.com/paper/2403.15067/) (2024)
- [Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data](https://mlsanity.com/paper/2111.08761/) (2021)
- [Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization](https://mlsanity.com/paper/2209.06332/) (2022)
- [Tiny, On-Device Decision Makers with the MiniConv Library](https://mlsanity.com/paper/2512.19726/) (2025)
- [Transferring Domain Knowledge with an Adviser in Continuous Tasks](https://mlsanity.com/paper/2102.08029/) (2021)
- [Training Lightweight CNNs for Human-Nanodrone Proximity Interaction from Small Datasets using Background Randomization](https://mlsanity.com/paper/2110.14491/) (2021)
- [Autoencoder for Synthetic to Real Generalization: From Simple to More Complex Scenes](https://mlsanity.com/paper/2204.00386/) (2022)
- [Robotic Packaging Optimization with Reinforcement Learning](https://mlsanity.com/paper/2303.14693/) (2023)
- [The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function](https://mlsanity.com/paper/2401.15856/) (2024)
- [An Open-source Sim2Real Approach for Sensor-independent Robot Navigation in a Grid](https://mlsanity.com/paper/2411.03494/) (2024)
- [Sustainable Smart Farm Networks: Enhancing Resilience and Efficiency with Decision Theory-Guided Deep Reinforcement Learning](https://mlsanity.com/paper/2505.03721/) (2025)
- [Dynamic-TD3: A Novel Algorithm for UAV Path Planning with Dynamic Obstacle Trajectory Prediction](https://mlsanity.com/paper/2605.00059/) (2026)
- [Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning](https://mlsanity.com/paper/2605.24449/) (2026)

## Abstract (arXiv)

Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, small-scale quadrotors. Simulated rendering and physics can provide for much larger datasets, but such data is inherently of lower quality: many of the phenomena that make the real-world autonomous flight problem challenging, such as complex physics and air currents, are modeled poorly or not at all, and the systematic differences between simulation and the real world are typically impossible to eliminate. In this work, we investigate how data from both simulation and the real world can be combined in a hybrid deep reinforcement learning algorithm. Our method uses real-world data to learn about the dynamics of the system, and simulated data to learn a generalizable perception system that can enable the robot to avoid collisions using only a monocular camera. We demonstrate our approach on a real-world nano aerial vehicle collision avoidance task, showing that with only an hour of real-world data, the quadrotor can avoid collisions in new environments with various lighting conditions and geometry. Code, instructions for building the aerial vehicles, and videos of the experiments can be found at github.com/gkahn13/GtS