ART/ATK: A research platform for assessing and mitigating the sim-to-real gap in robotics and autonomous vehicle engineering
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Abstract (arXiv)
We discuss a platform that has both software and hardware components, and whose purpose is to support research into characterizing and mitigating the sim-to-real gap in robotics and vehicle autonomy engineering. The software is operating-system independent and has three main components: a simulation engine called Chrono, which supports high-fidelity vehicle and sensor simulation; an autonomy stack for algorithm design and testing; and a development environment that supports visualization and hardware-in-the-loop experimentation. The accompanying hardware platform is a 1/6th scale vehicle augmented with reconfigurable mountings for computing, sensing, and tracking. Since this vehicle platform has a digital twin within the simulation environment, one can test the same autonomy perception, state estimation, or controls algorithms, as well as the processors they run on, in both simulation and reality. A demonstration is provided to show the utilization of this platform for autonomy research. Future work will concentrate on augmenting ART/ATK with support for a full-sized Chevy Bolt EUV, which will be made available to this group in the immediate future.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Nov 2022.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 7.
10 papers.
- 2022TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulationcited by 47
- 2023DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classificationcited by 23
- 2023APLA: Additional Perturbation for Latent Noise with Adversarial Training Enables Consistencycited by 9
- 2024CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimatorscited by 4
- 2024Learning Correspondence for Deformable Objectscited by 2
- 2024Safe Deep Model-Based Reinforcement Learning with Lyapunov Functionscited by 2
- 2024CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertaintycited by 2
- 2024Towards Improving Learning from Demonstration Algorithms via MCMC Methodscited by 0
- 2024Theoretical Analysis for Expectation-Maximization-Based Multi-Model 3D Registrationcited by 0
- 2024Rocket Landing Control with Grid Fins and Path-following using MPCcited by 0
- 2024CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty11 Dec 2024
- 2024CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators27 May 2024
- 2024Rocket Landing Control with Grid Fins and Path-following using MPC25 May 2024
- 2024Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions25 May 2024
- 2024Learning Correspondence for Deformable Objects14 May 2024
- 2024Theoretical Analysis for Expectation-Maximization-Based Multi-Model 3D Registration14 May 2024
- 2024Towards Improving Learning from Demonstration Algorithms via MCMC Methods3 May 2024
- 2023APLA: Additional Perturbation for Latent Noise with Adversarial Training Enables Consistency24 Aug 2023
- 2023DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification25 May 2023
- 2022TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulation17 Nov 2022