Grasp-TAGS: Task-Aware Grasp Selection via Critic Distillation for Prehensile Loco-Manipulation
Overview
This research presents Grasp-TAGS, a framework designed to improve large-object rearrangement by selecting optimal initial grasps for mobile manipulators. By training a unified reinforcement learning policy and distilling its critic into a lightweight network, the system predicts downstream task utility before contact is made. Experiments show an average success rate of 89.84%, outperforming fixed single-grasp policies (79.87%) and reducing orientation errors across various objects.
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Reported quantity · Reported success rate: 89.84 percent · reported trials · Differing source assertions
“the highest predicted downstream utility for the downstream policy to complete the task, before even making a contact. Experiments demonstrate that Grasp-TAGS improves rearrangement success over both fixed single-grasp policies and random grasp selection, achieving an average success rate of 89.84% compared with 79.87% for the best-performing single-grasp policies, an improvement of 9.97 percentage points. Grasp-TAGS”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 79.87 percent · reported trials · Differing source assertions
“the highest predicted downstream utility for the downstream policy to complete the task, before even making a contact. Experiments demonstrate that Grasp-TAGS improves rearrangement success over both fixed single-grasp policies and random grasp selection, achieving an average success rate of 89.84% compared with 79.87% for the best-performing single-grasp policies, an improvement of 9.97 percentage points. Grasp-TAGS”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalGrasp-TAGS: Task-Aware Grasp Selection via Critic Distillation for Prehensile Loco-Manipulation
This research presents Grasp-TAGS, a framework designed to improve large-object rearrangement by selecting optimal initial grasps for mobile manipulators. By training a unified reinforcement learning policy and distilling its critic into a lightweight network, the system predicts downstream task utility before contact is made. Experiments show an average success rate of 89.84%, outperforming fixed single-grasp policies (79.87%) and reducing orientation errors across various objects.
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