Consensus disagreement among off-the-shelf 6-DoF pose estimators, encoded as signed 6D differences, lets a lightweight MLP predict simulated grasp success more accurately than an ADD-based uncertainty baseline.
BOP: Benchmark for 6D Object Pose Estimation,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
dataset 1polarities
use dataset 1representative citing papers
citing papers explorer
-
Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
Consensus disagreement among off-the-shelf 6-DoF pose estimators, encoded as signed 6D differences, lets a lightweight MLP predict simulated grasp success more accurately than an ADD-based uncertainty baseline.