XPG-RL learns adaptive thresholds for switching between grasping, occlusion removal, and viewpoint adjustment, improving mechanical search efficiency by up to 4.5x over baselines.
A planning framework for non-prehensile manipulation under clutter and uncertainty
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
XPG-RL: Reinforcement Learning with Explainable Priority Guidance for Efficiency-Boosted Mechanical Search
XPG-RL learns adaptive thresholds for switching between grasping, occlusion removal, and viewpoint adjustment, improving mechanical search efficiency by up to 4.5x over baselines.