mlsanityData to 7 Oct 2026

Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors

arXiv 2201.01369 ↗First posted 4 Jan 2022cs.RO cs.AIcited by 4

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Abstract (arXiv)

In this work, we propose a data-driven approach to optimize the parameters of a simulation such that control policies can be directly transferred from simulation to a real-world quadrotor. Our neural network-based policies take only onboard sensor data as input and run entirely on the embedded hardware. In extensive real-world experiments, we compare low-level Pulse-Width Modulated control with higher-level control structures such as Attitude Rate and Attitude, which utilize Proportional-Integral-Derivative controllers to output motor commands. Our experiments show that low-level controllers trained with reinforcement learning require a more accurate simulation than higher-level control policies.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 4 Jan 2022.

Builds on

18 of 33 references.

References · 18

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2015Trust Region Policy Optimizationcited by 2,503
  4. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  5. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  6. 2018A Tutorial on Bayesian Optimizationcited by 734
  7. 2017Deep Reinforcement Learning that Matterscited by 727
  8. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 616
  9. 2019Learning agile and dynamic motor skills for legged robotscited by 402
  10. 2018Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experiencecited by 203
  11. 2021Learning High-Speed Flight in the Wildcited by 98
  12. 2021Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Controlcited by 68
6 more
  1. 2020Deep Drone Acrobaticscited by 66
  2. 2019Deep Drone Racing: From Simulation to Reality with Domain Randomizationcited by 63
  3. 2019Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learningcited by 40
  4. 2021Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policiescited by 37
  5. 2019Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotorscited by 36
  6. 2021Inclined Quadrotor Landing using Deep Reinforcement Learningcited by 5

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2023Learning to Fly in Secondscited by 18
  2. 2024What Matters in Learning A Zero-Shot Sim-to-Real RL Policy for Quadrotor Control? A Comprehensive Studycited by 9
  3. 2025RAPTOR: A Foundation Policy for Quadrotor Controlcited by 4
  4. 2025Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotorcited by 1