# Reinforcement Learning for Pivoting Task

arXiv: https://arxiv.org/abs/1703.00472 · HTML: https://mlsanity.com/paper/1703.00472/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2017.

## Built on by (most cited first, 17 of 17)

- [Real-World Humanoid Locomotion with Reinforcement Learning](https://mlsanity.com/paper/2303.03381/) (2023)
- [Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning](https://mlsanity.com/paper/2206.08686/) (2022)
- [Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment](https://mlsanity.com/paper/2104.05632/) (2021)
- [A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models](https://mlsanity.com/paper/2502.13187/) (2025)
- [Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability](https://mlsanity.com/paper/2209.08025/) (2022)
- [Learning Torque Control for Quadrupedal Locomotion](https://mlsanity.com/paper/2203.05194/) (2022)
- [DROPO: Sim-to-Real Transfer with Offline Domain Randomization](https://mlsanity.com/paper/2201.08434/) (2022)
- [Domain Randomization via Entropy Maximization](https://mlsanity.com/paper/2311.01885/) (2023)
- [Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets](https://mlsanity.com/paper/2310.04517/) (2023)
- [DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control](https://mlsanity.com/paper/2306.09010/) (2023)
- [Online vs. Offline Adaptive Domain Randomization Benchmark](https://mlsanity.com/paper/2206.14661/) (2022)
- [Unsupervised Feature Learning for Manipulation with Contrastive Domain Randomization](https://mlsanity.com/paper/2103.11144/) (2021)
- [Dropout's Dream Land: Generalization from Learned Simulators to Reality](https://mlsanity.com/paper/2109.08342/) (2021)
- [Shaping Laser Pulses with Reinforcement Learning](https://mlsanity.com/paper/2503.00499/) (2025)
- [The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective](https://mlsanity.com/paper/2606.07017/) (2026)
- [ORCA: A Platform for Open-Source Dexterity Research](https://mlsanity.com/paper/2606.14561/) (2026)
- [Deep Reinforcement Learning for Spacecraft Attitude Control During Atmospheric Re-Entry](https://mlsanity.com/paper/2606.31291/) (2026)

## Abstract (arXiv)

In this work we propose an approach to learn a robust policy for solving the pivoting task. Recently, several model-free continuous control algorithms were shown to learn successful policies without prior knowledge of the dynamics of the task. However, obtaining successful policies required thousands to millions of training episodes, limiting the applicability of these approaches to real hardware. We developed a training procedure that allows us to use a simple custom simulator to learn policies robust to the mismatch of simulation vs robot. In our experiments, we demonstrate that the policy learned in the simulator is able to pivot the object to the desired target angle on the real robot. We also show generalization to an object with different inertia, shape, mass and friction properties than those used during training. This result is a step towards making model-free reinforcement learning available for solving robotics tasks via pre-training in simulators that offer only an imprecise match to the real-world dynamics.