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FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements, , and . International Journal of Robotics Research, 33 (2): 268--304 (Nov 15, 2013)Particle Gaussian mixture filters-I., and . Autom., (2018)Sensor Scheduling Under Action Dependent Decision-Making Epochs., , , and . FUSION, page 1-7. IEEE, (2019)Model and Data Based Approaches to the Control of Tensegrity Robots., , , and . IEEE Robotics Autom. Lett., 5 (3): 3846-3853 (2020)On the Convergence of Reinforcement Learning., , and . CoRR, (2020)Structure Aided Odometry (SAO) : A Novel Analytical Odometry Technique Based on Semi-Absolute Localization for Precision-Warehouse Robotic Assistance in Environments with Low Feature Variation., , , and . J. Intell. Robotic Syst., 102 (4): 72 (2021)A Hybrid Bayesian-Frequentist Approach to SLAM., and . J. Intell. Robotic Syst., 83 (3-4): 561-583 (2016)On the probabilistic completeness of the sampling-based feedback motion planners in belief space., , and . ICRA, page 3983-3990. IEEE, (2012)Feedback motion planning under non-Gaussian uncertainty and non-convex state constraints., , , and . ICRA, page 4238-4244. IEEE, (2016)A computationally optimal randomized proper orthogonal decomposition technique., and . ACC, page 3310-3315. IEEE, (2016)