# Reinforced Grounded Action Transformation for Sim-to-Real Transfer

arXiv: https://arxiv.org/abs/2008.01279 · HTML: https://mlsanity.com/paper/2008.01279/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 18 of 18)

- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation](https://mlsanity.com/paper/2203.15041/) (2022)
- [From Motor Control to Team Play in Simulated Humanoid Football](https://mlsanity.com/paper/2105.12196/) (2021)
- [A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models](https://mlsanity.com/paper/2502.13187/) (2025)
- [Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning](https://mlsanity.com/paper/2308.14284/) (2023)
- [ASID: Active Exploration for System Identification in Robotic Manipulation](https://mlsanity.com/paper/2404.12308/) (2024)
- [Policy Transfer across Visual and Dynamics Domain Gaps via Iterative Grounding](https://mlsanity.com/paper/2107.00339/) (2021)
- [STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience](https://mlsanity.com/paper/2309.15302/) (2023)
- [Policy Learning for Off-Dynamics RL with Deficient Support](https://mlsanity.com/paper/2402.10765/) (2024)
- [Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control](https://mlsanity.com/paper/2307.12388/) (2023)
- [Zero-shot Sim2Real Adaptation Across Environments](https://mlsanity.com/paper/2302.04013/) (2023)
- [The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective](https://mlsanity.com/paper/2606.07017/) (2026)
- [A Q-learning approach to the continuous control problem of robot inverted pendulum balancing](https://mlsanity.com/paper/2312.02649/) (2023)
- [Off-dynamics Conditional Diffusion Planners](https://mlsanity.com/paper/2410.12238/) (2024)
- [Dual Action Policy for Robust Sim-to-Real Reinforcement Learning](https://mlsanity.com/paper/2410.12250/) (2024)
- [Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control](https://mlsanity.com/paper/2507.15174/) (2025)
- [Abstract Sim2Real through Approximate Information States](https://mlsanity.com/paper/2604.15289/) (2026)
- [TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation](https://mlsanity.com/paper/2606.06218/) (2026)

## Abstract (arXiv)

Robots can learn to do complex tasks in simulation, but often, learned behaviors fail to transfer well to the real world due to simulator imperfections (the reality gap). Some existing solutions to this sim-to-real problem, such as Grounded Action Transformation (GAT), use a small amount of real-world experience to minimize the reality gap by grounding the simulator. While very effective in certain scenarios, GAT is not robust on problems that use complex function approximation techniques to model a policy. In this paper, we introduce Reinforced Grounded Action Transformation(RGAT), a new sim-to-real technique that uses Reinforcement Learning (RL) not only to update the target policy in simulation, but also to perform the grounding step itself. This novel formulation allows for end-to-end training during the grounding step, which, compared to GAT, produces a better grounded simulator. Moreover, we show experimentally in several MuJoCo domains that our approach leads to successful transfer for policies modeled using neural networks.