CoFL learns continuous flow fields from BEV images and language instructions to generate navigation trajectories, outperforming modular VLM planners and trajectory policies on unseen scenes.
Deploying foundation model-enabled air and ground robots in the field: Challenges and opportunities
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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.
citing papers explorer
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CoFL: Continuous Flow Fields for Language-Conditioned Navigation
CoFL learns continuous flow fields from BEV images and language instructions to generate navigation trajectories, outperforming modular VLM planners and trajectory policies on unseen scenes.
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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.