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We present a motion planning algorithm for a class of uncertain control-affine nonlinear systems which guarantees runtime safety and goal reachability when using high-dimensional sensor measurements (e.g., RGB-D images) and a learned perception module in the feedback control loop.
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Manchester, I.R., Slotine, J.E.: Control contraction metrics: Convex and intrinsic criteria for nonlinear feedback design. IEEE Trans. Autom. Control. 62
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Dean, S., Matni, N., Recht, B., Ye, V.: Robust guarantees for perception-based control. In: L4DC. vol. 120, pp. 350–360. PMLR (2020)
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Cosner, R., Singletary, A., Taylor, A., Molnár, T., Bouman, K., Ames, A.: Measurement-robust control barrier functions: Certainty in safety with uncertainty in state. In: IROS (2021)
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Dean, S., Taylor, A.J., Cosner, R.K., Recht, B., Ames, A.D.: Guaranteeing safety of learned perception modules via measurement-robust control barrier functions. In: CoRL (2020)
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Renganathan, V., Shames, I., Summers, T.H.: Towards integrated perception and motion planning with distributionally robust risk constraints. IFAC World Congress (2020)
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Sun, D., Jha, S., Fan, C.: Learning certified control using contraction metric. CoRL (2020)
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Veer, S., Majumdar, A.: Probably approximately correct vision-based planning using motion primitives. In: CoRL. vol. 155, pp. 1001–1014. PMLR (2020)
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Bahreinian, M., Mitjans, M., Tron, R.: Robust sample-based output-feedback path planning. In: IROS. pp. 5780–5787. IEEE (2021)
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Tsukamoto, H., Chung, S.: Learning-based robust motion planning with guaranteed stability: A contraction theory approach. RA-L 6
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Tsukamoto, H., Chung, S.: Neural contraction metrics for robust estimation and control: A convex optimization approach. IEEE CSL 5
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Dawson, C., Lowenkamp, B., Goff, D., Fan, C.: Learning safe, generalizable perception-based hybrid control with certificates. RA-L (2022)
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