Introduces UAV-VLN-FOV task and 3DG-VLN framework for precise target-visible UAV navigation, reporting 13.82% success rate gain on a new 2,717-trajectory benchmark with code released.
arXiv preprint arXiv:2603.14363 , year=
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
AIR-VLA+ introduces cascaded manipulation and movement decoders plus asymmetric MoE to decouple action scales in aerial manipulation, reporting 48.0 average score and 80.2% task completion gain over single-head baseline on AIR-VLA benchmark.
Introduces CARLA-Air simulator for air-ground VLA evaluation and shows that current aerial VLA models track ground partners but fail to achieve stable cooperative behavior under text-based interfaces.
A zero-shot unified agent for VLN-CE, ObjectNav, EQA and Aerial-VLN on wheeled, quadruped, humanoid and UAV platforms that translates language and vision inputs into actions via MLLMs plus TDM and SCB mechanisms, matching trained foundation models on multiple benchmarks.
LiteVLA-H delivers 19.74 Hz action tokens and 6 Hz semantic outputs on Jetson Orin via dual-rate scheduling and mixed fine-tuning, outperforming recent VLA baselines in edge action rate while preserving descriptive competence.
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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See-and-Reach: Precise Vision-Language Navigation for UAVs within the Field of View
Introduces UAV-VLN-FOV task and 3DG-VLN framework for precise target-visible UAV navigation, reporting 13.82% success rate gain on a new 2,717-trajectory benchmark with code released.
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AIR-VLA+: Decoupling Movement and Manipulation via Cascaded Dual-Action Decoders with Asymmetric MoE for Aerial Robots
AIR-VLA+ introduces cascaded manipulation and movement decoders plus asymmetric MoE to decouple action scales in aerial manipulation, reporting 48.0 average score and 80.2% task completion gain over single-head baseline on AIR-VLA benchmark.
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Can Aerial VLA Models Cooperate? Evaluating Closed-Loop Air-Ground Coordination with CARLA-Air
Introduces CARLA-Air simulator for air-ground VLA evaluation and shows that current aerial VLA models track ground partners but fail to achieve stable cooperative behavior under text-based interfaces.
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Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation
A zero-shot unified agent for VLN-CE, ObjectNav, EQA and Aerial-VLN on wheeled, quadruped, humanoid and UAV platforms that translates language and vision inputs into actions via MLLMs plus TDM and SCB mechanisms, matching trained foundation models on multiple benchmarks.
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LiteVLA-H: Dual-Rate Vision-Language-Action Inference for Onboard Aerial Guidance and Semantic Perception
LiteVLA-H delivers 19.74 Hz action tokens and 6 Hz semantic outputs on Jetson Orin via dual-rate scheduling and mixed fine-tuning, outperforming recent VLA baselines in edge action rate while preserving descriptive competence.
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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.