A novel tri-plane equivariant volumetric grasp model adapts GIGA and IGD planners with flow matching and deformable attention to achieve higher real-time performance than non-equivariant baselines.
Synergies between affordance and geometry: 6-dof grasp detection via implicit representa- tions
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A graspability field learned from synthesized grasps provides a dense reward signal for an RL policy that performs closed-loop non-prehensile manipulation leading to successful grasps.
AISPO proposes a depth completion method using multi-scale RGB-D fusion and an affine-invariant shape prior to improve depth reliability and manipulation success for non-Lambertian objects.
Language-guided object-centric completion plus execution-aware grasp selection raised real Spot grasp success from 3/10 to 9/10 under partial observation in cluttered tabletop scenes.
citing papers explorer
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Equivariant Volumetric Grasping
A novel tri-plane equivariant volumetric grasp model adapts GIGA and IGD planners with flow matching and deformable attention to achieve higher real-time performance than non-equivariant baselines.
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Grasp-Oriented Non-Prehensile Manipulation via Learning a Graspability Field
A graspability field learned from synthesized grasps provides a dense reward signal for an RL policy that performs closed-loop non-prehensile manipulation leading to successful grasps.
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AISPO: Enhancing Depth Reliability for Robotic Manipulation of Non-Lambertian Objects via Affine-Invariant Shape Prior
AISPO proposes a depth completion method using multi-scale RGB-D fusion and an affine-invariant shape prior to improve depth reliability and manipulation success for non-Lambertian objects.
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Language-Guided Grasping under Partial Observation for Mobile Manipulation in Field Inspection and Maintenance
Language-guided object-centric completion plus execution-aware grasp selection raised real Spot grasp success from 3/10 to 9/10 under partial observation in cluttered tabletop scenes.