CPNav: Calibrated Vision-Language Navigation for Object-Goal Search in Unseen Environments
Overview
This paper presents CPNav, a navigation framework combining collision-aware planning with statistically calibrated Vision-Language Model action scoring for map-free object-goal navigation. Using RGB-D input, the system constructs navigability maps and queries two VLMs for goal detection and action scoring, with offline calibration via conformal prediction providing finite-sample guarantees. Evaluated in AI Habitat simulation and on a modified Hiwonder MentorPi robot, CPNav achieves significant relative improvements in success rate and success weighted path length compared to uncalibrated baselines.
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“ovements of 6.2% and 11.4% on HM3D v0.2, and 12.4% and 8.8% on MP3D, respectively. In real-world deployment on a modified Hiwonder MentorPi robot, the corresponding improvements in SR and SPL are 24.9% and 15.2%, respectively. Code is available at https://github.com/practice-lab-ucla/CPnav.”
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- arXiv Robotics — research abstractsSignalCPNav: Calibrated Vision-Language Navigation for Object-Goal Search in Unseen Environments
This paper presents CPNav, a navigation framework combining collision-aware planning with statistically calibrated Vision-Language Model action scoring for map-free object-goal navigation. Using RGB-D input, the system constructs navigability maps and queries two VLMs for goal detection and action scoring, with offline calibration via conformal prediction providing finite-sample guarantees. Evaluated in AI Habitat simulation and on a modified Hiwonder MentorPi robot, CPNav achieves significant relative improvements in success rate and success weighted path length compared to uncalibrated baselines.
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