# Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics

arXiv: https://arxiv.org/abs/2502.07380 · HTML: https://mlsanity.com/paper/2502.07380/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 34 of its 54 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 30 of 34)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Eureka: Human-Level Reward Design via Coding Large Language Models](https://mlsanity.com/paper/2310.12931/) (2023)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI](https://mlsanity.com/paper/2410.00425/) (2024)
- [RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening](https://mlsanity.com/paper/2103.15597/) (2021)
- [RoboTHOR: An Open Simulation-to-Real Embodied AI Platform](https://mlsanity.com/paper/2004.06799/) (2020)
- [ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots](https://mlsanity.com/paper/2306.14874/) (2023)
- [Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping](https://mlsanity.com/paper/1709.07857/) (2017)
- [Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning](https://mlsanity.com/paper/2407.15815/) (2024)
- [V-STRONG: Visual Self-Supervised Traversability Learning for Off-road Navigation](https://mlsanity.com/paper/2312.16016/) (2023)
- [Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight](https://mlsanity.com/paper/1902.03701/) (2019)
- [Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing](https://mlsanity.com/paper/2103.04909/) (2021)
- [RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving](https://mlsanity.com/paper/2402.19341/) (2024)
- [High-speed Autonomous Drifting with Deep Reinforcement Learning](https://mlsanity.com/paper/2001.01377/) (2020)
- [Berkeley Humanoid: A Research Platform for Learning-based Control](https://mlsanity.com/paper/2407.21781/) (2024)
- [Toward Wheeled Mobility on Vertically Challenging Terrain: Platforms, Datasets, and Algorithms](https://mlsanity.com/paper/2303.00998/) (2023)
- [MuSHR: A Low-Cost, Open-Source Robotic Racecar for Education and Research](https://mlsanity.com/paper/1908.08031/) (2019)
- [FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing](https://mlsanity.com/paper/2304.09831/) (2023)
- [DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control](https://mlsanity.com/paper/2310.09053/) (2023)
- [AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility](https://mlsanity.com/paper/2409.15783/) (2024)
- [A Multi-step Dynamics Modeling Framework For Autonomous Driving In Multiple Environments](https://mlsanity.com/paper/2305.02241/) (2023)
- [Model Predictive Control for Aggressive Driving Over Uneven Terrain](https://mlsanity.com/paper/2311.12284/) (2023)
- [Unifying F1TENTH Autonomous Racing: Survey, Methods and Benchmarks](https://mlsanity.com/paper/2402.18558/) (2024)
- [PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain](https://mlsanity.com/paper/2409.03005/) (2024)
- [Reinforcement Learning for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.02383/) (2024)
- [Agile Continuous Jumping in Discontinuous Terrains](https://mlsanity.com/paper/2409.10923/) (2024)

## Built on by (most cited first, 3 of 3)

- [Model Predictive Adversarial Imitation Learning for Planning from Observation](https://mlsanity.com/paper/2507.21533/) (2025)
- [Reset-Free Reinforcement Learning for Real-World Agile Driving: An Empirical Study](https://mlsanity.com/paper/2604.07672/) (2026)
- [RobotWorld: Benchmarking Multimodal Agents for Robot Use Across Diverse Tasks and Embodiments](https://mlsanity.com/paper/2610.10409/) (2026)

## Abstract (arXiv)

Reinforcement Learning (RL) has been pivotal in recent robotics milestones and is poised to play a prominent role in the future. However, these advances can rely on proprietary simulators, expensive hardware, and a daunting range of tools and skills. As a result, broader communities are disconnecting from the state-of-the-art; education curricula are poorly equipped to teach indispensable modern robotics skills involving hardware, deployment, and iterative development. To address this gap between the broader and scientific communities, we contribute Wheeled Lab, an ecosystem which integrates accessible, open-source wheeled robots with Isaac Lab, an open-source robot learning and simulation framework, that is widely adopted in the state-of-the-art. To kickstart research and education, this work demonstrates three state-of-the-art zero-shot policies for small-scale RC cars developed through Wheeled Lab: controlled drifting, elevation traversal, and visual navigation. The full stack, from hardware to software, is low-cost and open-source. Videos and additional materials can be found at: https://uwrobotlearning.github.io/WheeledLab/