AURA combines online replanning and optimization into an asymptotically optimal framework that improves trajectory quality and tracking accuracy under uncertainty for kinodynamic systems.
An MPC framework for efficient navigation of mobile robots in cluttered environments,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
AirDreamer combines world-model-based environment understanding with an RL policy and sparse rewards to navigate unseen environments, achieving 5.3% higher success than baselines and effective sim-to-real transfer without tuning.
citing papers explorer
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AURA: Asymptotically Optimal Uncertainty-Robust Replanning Algorithm for Kinodynamic Systems
AURA combines online replanning and optimization into an asymptotically optimal framework that improves trajectory quality and tracking accuracy under uncertainty for kinodynamic systems.
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AirDreamer: Generalist Drone Navigation with World Models
AirDreamer combines world-model-based environment understanding with an RL policy and sparse rewards to navigate unseen environments, achieving 5.3% higher success than baselines and effective sim-to-real transfer without tuning.