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.
Sampling-based motion planning: A comparative review
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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.
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.
citing papers explorer
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Terminal Matters: Kinodynamic Planning with a Terminal Cost and Learned Uncertainty in Belief State-Cost Space
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.
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Utilizing Inpainting for Keypoint Detection for Vision-Based Control of Robotic Manipulators
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.
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ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation
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.