Guarantees for Real Robotic Systems: Unifying Formal Controller Synthesis and Reachset-Conformant Identification
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Abstract (arXiv)
Robots are used increasingly often in safety-critical scenarios, such as robotic surgery or human-robot interaction. To ensure stringent performance criteria, formal controller synthesis is a promising direction to guarantee that robots behave as desired. However, formally ensured properties only transfer to the real robot when the model is appropriate. We address this problem by combining the identification of a reachset-conformant model with controller synthesis. Since the reachset-conformant model contains all the measured behaviors of the real robot, the safety properties of the model transfer to the real robot. The transferability is demonstrated by experiments on a real robot, for which we synthesize tracking controllers.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Mar 2021.
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Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
- 2025From Demonstrations to Safe Deployment: Path-Consistent Safety Filtering for Diffusion Policiescited by 4
- 2024CommonPower: A Framework for Safe Data-Driven Smart Grid Controlcited by 3
- 2025Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Taskscited by 2
- 2025Leveraging Analytic Gradients in Provably Safe Reinforcement Learningcited by 1
- 2026Multi-Variable Conformal Prediction: Optimizing Prediction Sets without Data Splittingcited by 0
- 2026Multi-Variable Conformal Prediction: Optimizing Prediction Sets without Data Splitting12 May 2026
- 2025From Demonstrations to Safe Deployment: Path-Consistent Safety Filtering for Diffusion Policies9 Nov 2025
- 2025Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks14 Aug 2025
- 2025Leveraging Analytic Gradients in Provably Safe Reinforcement Learning2 Jun 2025
- 2024CommonPower: A Framework for Safe Data-Driven Smart Grid Control5 Jun 2024