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VLN on the Fly, an onboard stack that keeps grounding, planning, and control as separate, inspectable stages that reaches the target in 13 of 15 trials with 5.72 cm mean goal error and 39.3% average GPU utilization.
In this controlled setting, object-centric completion and execution-aware selection reduced approach collisions and improved the reliability of language-guided grasping for supervised field manipulation.
A shared-autonomy framework that assists the operator throughout this process ofTeleoperating a robotic manipulator in industrial environments demands precision that camera-based interfaces alone struggle to deliver and is validated on a quadruped mobile manipulator.
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