mlsanityData to 14 Oct 2026

Zero-shot Transfer of Reinforcement Learning Control Policies for the Swing-Up and Stabilization of a Cart-Pole System

arXiv 2606.22145 ↗First posted 20 Jun 2026cs.RO cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

Reinforcement learning (RL) is a powerful and convenient tool to modernize controller design. In this work, we study the zero-shot transfer of RL-based control policies from simulation to hardware for cart-pole swing-up and stabilization. The two policies are trained independently, and the handoff is implemented in Simulink via switching logic. We apply a first-order action smoothing filter to prevent hardware damage from high-frequency oscillatory actuation. Pairing this bandwidth-aware filtering with sensitivity-guided domain randomization (DR) and a simple linear curriculum learning (CL) schedule, we obtain a swing-up policy that in all of our experiments injects sufficient energy for handoff into the stabilizer's region of attraction. The stabilization policy rejects disturbances within the tested range, and the swing-up policy can re-engage after larger perturbations and restores the pendulum to the inverted position.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 22 of its 57 references that are in the map. First posted on arXiv 20 Jun 2026.

Builds on

22 of 57 references.

References · 22

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  4. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  5. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 413
  6. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 373
  7. 2018A Tour of Reinforcement Learning: The View from Continuous Controlcited by 151
  8. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Controlcited by 99
  9. 2016Sim-to-Real Robot Learning from Pixels with Progressive Netscited by 94
  10. 2019BayesSim: adaptive domain randomization via probabilistic inference for robotics simulatorscited by 85
  11. 2021Understanding Domain Randomization for Sim-to-real Transfercited by 80
  12. 2025MuJoCo Playgroundcited by 78
10 more
  1. 2021Robot Learning from Randomized Simulations: A Reviewcited by 63
  2. 2021Auto-Tuned Sim-to-Real Transfercited by 49
  3. 2018A survey on policy search algorithms for learning robot controllers in a handful of trialscited by 35
  4. 2019Assessing Transferability from Simulation to Reality for Reinforcement Learningcited by 33
  5. 2022Lyapunov Design for Robust and Efficient Robotic Reinforcement Learningcited by 28
  6. 2024Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RLcited by 21
  7. 2019PAC Reinforcement Learning without Real-World Feedbackcited by 6
  8. 2025Integrating Model-based Control and RL for Sim2Real Transfer of Tight Insertion Policiescited by 4
  9. 2023AUGCAL: Improving Sim2Real Adaptation by Uncertainty Calibration on Augmented Synthetic Imagescited by 3
  10. 2025Control Synthesis with Reinforcement Learning: A Modeling Perspectivecited by 1

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  1. 2026Reinforcement Learning for Control with Probabilistic Stability Guarantee: A Finite-Sample Approach
  2. 2024Technical Report on Reinforcement Learning Control on the Lucas-N"ulle Inverted Pendulum
  3. 2025Zero-Shot Policy Transfer in Reinforcement Learning using Buckingham's Pi Theorem
  4. 2026Reinforcement Learning for Elliptical Cylinder Motion Control Tasks ↗
  5. 2024Reinforcement Learning for Motor Control: A Comprehensive Review
  6. 2026Maximum Entropy Behavior Exploration for Sim2Real Zero-Shot Reinforcement Learning
  7. 2025RoVerFly: Robust and Versatile Implicit Hybrid Control of Quadrotor-Payload Systems
  8. 2026Actuator Reality Shaping for Zero-Shot Sim-to-Real Robot Learning ↗