Fibration trees unify projections and decompositions for multi-robot planning; Fibration-RRT generalizes quotient and discrete RRT, is probabilistically complete, and solves problems up to 96 DOF via user-defined trees.
Sampling-based motion p lanning: A comparative review,
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KiTe augments sampling-based kinodynamic planning with terminal costs in belief space, proving asymptotic optimality preservation and improved goal-reaching probability bounds via Wasserstein minimization, supported by learned uncertainty models and experiments.
A framework trains keypoint detectors on inpainted markerless robot images and uses runtime inpainting plus UKF for robust vision-based control without models or calibration.
AURA combines online replanning and optimization into an asymptotically optimal framework that improves trajectory quality and tracking accuracy under uncertainty for kinodynamic systems.
ActivePusher integrates residual-physics modeling with uncertainty-based active learning to improve data efficiency and planning success rates for nonprehensile manipulation in simulation and real-world settings.
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Fibration Trees: A Unified Approach to Multi-Robot Motion Planning
Fibration trees unify projections and decompositions for multi-robot planning; Fibration-RRT generalizes quotient and discrete RRT, is probabilistically complete, and solves problems up to 96 DOF via user-defined trees.