# Stochastic Grounded Action Transformation for Robot Learning in Simulation

arXiv: https://arxiv.org/abs/2008.01281 · HTML: https://mlsanity.com/paper/2008.01281/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation](https://mlsanity.com/paper/2203.15041/) (2022)
- [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)
- [VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics](https://mlsanity.com/paper/2203.15983/) (2022)
- [High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization](https://mlsanity.com/paper/2206.08487/) (2022)
- [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)
- [Mimicking Better by Matching the Approximate Action Distribution](https://mlsanity.com/paper/2306.09805/) (2023)
- [Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control](https://mlsanity.com/paper/2307.12388/) (2023)
- [A Multi-Fidelity Control Variate Approach for Policy Gradient Estimation](https://mlsanity.com/paper/2503.05696/) (2025)
- [Fast Model-based Policy Search for Universal Policy Networks](https://mlsanity.com/paper/2202.05843/) (2022)
- [Tiny Reinforcement Learning for Quadruped Locomotion using Decision Transformers](https://mlsanity.com/paper/2402.13201/) (2024)
- [Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality](https://mlsanity.com/paper/2510.17709/) (2025)
- [The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective](https://mlsanity.com/paper/2606.07017/) (2026)
- [Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control](https://mlsanity.com/paper/2507.15174/) (2025)
- [TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation](https://mlsanity.com/paper/2606.06218/) (2026)

## Abstract (arXiv)

Robot control policies learned in simulation do not often transfer well to the real world. Many existing solutions to this sim-to-real problem, such as the Grounded Action Transformation (GAT) algorithm, seek to correct for or ground these differences by matching the simulator to the real world. However, the efficacy of these approaches is limited if they do not explicitly account for stochasticity in the target environment. In this work, we analyze the problems associated with grounding a deterministic simulator in a stochastic real world environment, and we present examples where GAT fails to transfer a good policy due to stochastic transitions in the target domain. In response, we introduce the Stochastic Grounded Action Transformation(SGAT) algorithm,which models this stochasticity when grounding the simulator. We find experimentally for both simulated and physical target domains that SGAT can find policies that are robust to stochasticity in the target domain