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The interest in using reinforcement learning (RL) controllers in safety-critical applications such as robot navigation around pedestrians motivates the development of additional safety mechanisms.
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R. Luo, S. Zhao, J. Kuck, B. Ivanovic, S. Savarese, E. Schmerling, and M. Pavone, “Sample-efficient safety assurances using conformal prediction,” in Algorithmic Foundations of Robotics XV: Proceedings of the Fifteenth Workshop on the Algorithmic Foundations of Robotics . Springer, 2022, pp. 149–169
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K. P. Wabersich and M. N. Zeilinger, “Predictive control barrier functions: Enhanced safety mechanisms for learning-based control,” IEEE Transactions on Automatic Control , 2022
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2023
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L. Lindemann, M. Cleaveland, G. Shim, and G. J. Pappas, “Safe planning in dynamic environments using conformal prediction,” IEEE Robotics and Automation Letters , vol. 8, no. 8, pp. 5116–5123, 2023
2023
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N. Rober, S. M. Katz, C. Sidrane, E. Yel, M. Everett, M. J. Kochenderfer, and J. P. How, “Backward reachability analysis of neural feedback loops: Techniques for linear and nonlinear systems,” IEEE Open Journal of Control Systems , 2023
2023
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M. Fontana, G. Zeni, and S. Vantini, “Conformal prediction: a unified review of theory and new challenges,” Bernoulli , vol. 29, no. 1, pp. 1–23, 2023
2023
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2023
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2023
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M. Pfeiffer, U. Schwesinger, H. Sommer, E. Galceran, and R. Siegwart, “Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models,” in 2016 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 2096–2101
2096
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