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PONI: Potential Functions for ObjectGoal Navigation With Interaction-Free Learning

CVPR 2022 · Detection and segmentation › Open-set and few-shot detection · approach: Hand-object and articulated interaction · 147 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace SemExp's interactive long-term-goal learning with PONI's interaction-free potential predictionObjectNav · Gibson (val)SPL: 39.6 -> 41.0Helped
use PONI rather than THDAObjectNav · MP3D (val)SPL: 11.0 -> 12.1Helped
predict area and object potential functions instead of directly predicting object direction or locationPONI's semantic-map policy · Gibson (val)Success: 69.9 -> 73.6Helped
add the area potential to the object potential (ablation)PONI · Gibson (val)Success: 58.8 -> 73.6Helped
add the area potential to the object potential (ablation)PONI · MP3D (val)Success: 30.5 -> 31.8Helped
add the object potential to area-potential exploration (ablation)PONI · Gibson (val)Success: 65.1 -> 73.6Helped
add the object potential to area-potential exploration (ablation)PONI · MP3D (val)Success: 30.8 -> 31.8Helped
define both potentials only at map frontiers (ablation)PONI · Gibson (val)Success: 72.7 -> 73.6Helped
replace predicted image segmentation with ground-truth segmentation (ablation)PONI · Gibson (val)Success: 73.6 -> 86.5Helped
replace predicted image segmentation with ground-truth segmentation (ablation)PONI · MP3D (val)Success: 31.8 -> 58.2Helped
use view-cone rather than square masks to generate potential-network training samples (ablation)PONI · MP3D (val)SPL: 12.1 -> 12.1No clear effect
add distance-based action cost when sampling long-term goals (ablation)PONI · MP3D (val)Success: 31.8 -> 30.3Hurt

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