HiPAN enables quadruped robots to navigate unstructured 3D environments more successfully by combining a high-level posture-adaptive policy with a low-level controller and curriculum learning on depth images.
Nebula: Quest for robotic autonomy in challenging environments; team costar at the darpa subterranean challenge
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HRCD-FLP model shows cost reductions from shifting human-robot supervision from 1:3 to 1:10 while maintaining full critical site coverage, with heuristics solving large instances in minutes.
QuadPiPS combines semantic affordance prediction with an egocentric legged egocan representation and trajectory optimization to produce kinodynamically feasible foothold plans that outperform baselines in limited-foothold settings and run on hardware.
Derives and field-validates a KPI framework for multi-robot lunar prospecting that prioritizes efficiency, robustness, and precision based on three realistic scenarios.
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.
citing papers explorer
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HiPAN: Hierarchical Posture-Adaptive Navigation for Quadruped Robots in Unstructured 3D Environments
HiPAN enables quadruped robots to navigate unstructured 3D environments more successfully by combining a high-level posture-adaptive policy with a low-level controller and curriculum learning on depth images.
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Optimized Human-Robot Co-Dispatch Planning for Petro-Site Surveillance under Varying Criticalities
HRCD-FLP model shows cost reductions from shifting human-robot supervision from 1:3 to 1:10 while maintaining full critical site coverage, with heuristics solving large instances in minutes.
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QuadPiPS: A Perception-informed Footstep Planner for Quadrupeds With Semantic Affordance Prediction
QuadPiPS combines semantic affordance prediction with an egocentric legged egocan representation and trajectory optimization to produce kinodynamically feasible foothold plans that outperform baselines in limited-foothold settings and run on hardware.
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A Practical Framework of Key Performance Indicators for Multi-Robot Lunar and Planetary Field Tests
Derives and field-validates a KPI framework for multi-robot lunar prospecting that prioritizes efficiency, robustness, and precision based on three realistic scenarios.
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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.