arXiv AI
Jul 17
Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Execution
arXiv:2607. 14341v1 Announce Type: cross Abstract: Robust robotic grasping remains a fundamental challenge for complex real-world applications.
GraRe: Grasp Candidate Re-Ranking for Frozen 6-DoF Grasp Detectors
arXiv:2608. 00946v1 Announce Type: cross Abstract: Existing 6-DoF grasp detectors typically rank grasp candidates by detector confidence.
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arXiv AI
Jun 19
Human Universal Grasping
arXiv:2606. 17054v1 Announce Type: cross Abstract: Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality.
arXiv AI
Jul 1
Agentic RAG-VLM: Affordance-Aware Retrieval-Augmented Generation with Self-Reflective Planning for Robotic Grasping
arXiv:2606. 31200v1 Announce Type: new Abstract: Generalizable robotic grasping in cluttered environments is essential for deploying manipulators in unstructured human spaces, yet existing VLM-based methods rely on visual similarity for object matching, neglecting physical affordances such as handle graspability and material fragility, and operate open-loop without spatial reasoning or failure recovery, limiting their effectiveness when objects are densely packed or physically diverse.
arXiv Machine Learning
Jul 7
Language-Guided Grasping under Partial Observation for Mobile Manipulation in Field Inspection and Maintenance
arXiv:2603. 07866v3 Announce Type: replace-cross Abstract: Offshore inspection and maintenance have increasingly been using legged robots for routine sensing, yet many useful interventions still require physical interaction with tools, containers, and task-relevant objects.
arXiv AI
Jul 21
Seg2Grasp: A Robust Modular Suction Grasping in Bin Picking
arXiv:2607. 17757v1 Announce Type: cross Abstract: Current bin picking methods that rely heavily on end-to-end learning often falter when confronted with unfamiliar or complex objects in unstructured environments.
arXiv AI
Jul 1
Learning Dexterous Grasping from Sparse Taxonomy Guidance
arXiv:2604. 04138v2 Announce Type: replace-cross Abstract: Dexterous manipulation requires planning a grasp configuration suited to the object and task, which is then executed through coordinated multi-finger control.
arXiv AI
Jul 14
Pickalo: Leveraging 6D Pose Estimation for Low-Cost Industrial Bin Picking
arXiv:2604. 04690v2 Announce Type: replace-cross Abstract: Bin picking in real industrial environments remains challenging due to severe clutter, occlusions, and the high cost of traditional 3D sensing setups.
arXiv AI
Jul 29
SAM3D-Guided Object-Centric Representation Alignment for Vision-Language-Action Models
arXiv:2607. 25912v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for general robot manipulation, but most existing models rely on 2D visual-language backbones and lack fine-grained 3D understanding of target objects, especially under occlusion, pose variation, scale changes, and precise spatial interaction.
arXiv AI
Jun 9
GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation
arXiv:2606. 08530v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot embodiments.
arXiv AI
Jul 14
More Structure, Not More Capacity: Object-Centric Representations for Visuomotor Imitation Learning
arXiv:2607. 09825v1 Announce Type: cross Abstract: Robotic manipulation policies rely on pre-trained vision models that give either a global scene embedding or a dense patch grid.
arXiv AI
23h ago
Iterative Grasp Pose Refinement: A Deep Reinforcement Learning Approach for 2D Vision
arXiv:2608. 17628v1 Announce Type: cross Abstract: Developing robots capable of understanding and manipulating objects requires compact, interpretable, and generalizable representations.
arXiv AI
Jun 12
Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning
arXiv:2606. 12910v1 Announce Type: cross Abstract: For robotics to be effectively integrated into household or industrial environments, machines must adapt to natural-language prompts in real time.