{"total":12,"items":[{"citing_arxiv_id":"2606.30474","ref_index":29,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Grasp-Oriented Non-Prehensile Manipulation via Learning a Graspability Field","primary_cat":"cs.RO","submitted_at":"2026-06-29T15:33:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.21148","ref_index":10,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Pose-Agnostic Robotic Functional Grasping via Observation-Action Canonicalization","primary_cat":"cs.RO","submitted_at":"2026-06-19T06:37:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"AnyMug trains a single closed-loop visuomotor policy in simulation using observation-action canonicalization and deploys it zero-shot on a real robot for functional mug-handle grasping across poses.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20272","ref_index":22,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applications","primary_cat":"cs.RO","submitted_at":"2026-06-18T14:17:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"The paper reviews limits in AI vision for robotics and describes work-in-progress on bridging sim-to-real domain gaps by linking real and synthetic training data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20193","ref_index":24,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Belt-Finger: An Affordable Soft Belt-Driven Gripper for Dexterous In-Hand Manipulation","primary_cat":"cs.RO","submitted_at":"2026-06-18T13:07:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A double-soft-belt finger module adds translation, pitch, and roll to parallel grippers for improved in-hand manipulation at low cost.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.18053","ref_index":18,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"A Hybrid Optimization Framework for Grasp Synthesis under Partial Observations","primary_cat":"cs.RO","submitted_at":"2026-06-16T15:30:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Hybrid EBM-ICP grasp synthesis via SVGD reports 60.9% success on 67 objects with 5360 attempts, outperforming AnyGrasp, GPD, and AS-ICP baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00998","ref_index":39,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping","primary_cat":"cs.RO","submitted_at":"2026-05-31T04:28:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GraspGen-X extends diffusion 6-DOF grasping to cross-embodiment via swept-volume gripper encoding, trained on procedural grippers and 2B grasps, claiming best zero-shot generalization to novel grippers in sim and real tests.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21460","ref_index":31,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"HITL-D: Human In The Loop Diffusion Assisted Shared Control","primary_cat":"cs.RO","submitted_at":"2026-05-20T17:49:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"HITL-D combines diffusion policies with human input for shared robotic control, reducing required joystick axes and improving speed and workload in manipulation tasks per a 12-participant study.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21414","ref_index":45,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction","primary_cat":"cs.RO","submitted_at":"2026-05-20T17:10:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"PointACT proposes a 3D-aware dual-system VLA policy using multi-scale point-action interaction with bottleneck window self-attention, achieving 10% higher success rates on RLBench-10Tasks over prior pretrained VLAs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.03233","ref_index":39,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data","primary_cat":"cs.RO","submitted_at":"2025-05-06T06:59:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GraspVLA shows that pretraining a grasping model on a billion synthetic action frames enables zero-shot open-vocabulary performance and sim-to-real transfer.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.16054","ref_index":56,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"$\\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization","primary_cat":"cs.LG","submitted_at":"2025-04-22T17:31:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"π_{0.5} is a VLA model that achieves long-horizon dexterous manipulation in entirely new homes through co-training on heterogeneous tasks and multi-source data including web and semantic predictions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2210.00379","ref_index":155,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)","primary_cat":"cs.CV","submitted_at":"2022-10-01T21:35:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A literature survey of NeRF and neural field methods from 2020-2025, organized by architecture and application taxonomies with benchmarks and dataset overviews, covering both pre- and post-Gaussian Splatting periods.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"by certain rgb-d cameras such as RealSense. The paper also presents three novel datasets focused on transparent objects, one synthetic, and two real-world. Dex-NeRF improves upon baseline NeRF with respect to computed depths of transparent objects by using a fixed empirical threshold for density along rays. Their NeRF model is then used to produce a depth map used by Dex-Net [155] for grasp planning. Evo-NeRF [156] (November 2022) improves upon Dex-NeRF by reusing weights in sequential grasping, early termination, and an improved Radiance-Adjusted-Grasp Network capable of grasp planning with unreliable geometry. In the following subsections, we classify applications of NeRF methods into urban reconstruction, human and artic-"},{"citing_arxiv_id":"1907.10932","ref_index":18,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Object Perception and Grasping in Open-Ended Domains","primary_cat":"cs.RO","submitted_at":"2019-07-25T09:46:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"Research agenda posing questions on open-ended object perception and grasping for robots that learn categories and affordances gradually from experiences rather than from complete upfront training sets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}