Introduces a structured framework showing that visual predicate failures under degradation are non-uniform, with static predicates more robust than dynamic ones like grasp and release, and quantifies downstream accuracy drops.
Uncertainty-aware receding horizon exploration and mapping using aerial robots , isbn =
2 Pith papers cite this work. Polarity classification is still indexing.
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An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
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The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.