{"total":14,"items":[{"citing_arxiv_id":"2606.30749","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation","primary_cat":"cs.RO","submitted_at":"2026-06-29T18:00:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Grasp pretraining on 355k trajectories improves full-task success on six articulated tool-use tasks by 33.3 pp over DP3 in real-world experiments.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.28813","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning","primary_cat":"cs.RO","submitted_at":"2026-06-27T08:45:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Human2Any transfers human video demonstrations to robots by representing tasks as object-object interactions and composing learned priors with robot-side planning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12109","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning","primary_cat":"cs.RO","submitted_at":"2026-06-10T14:03:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"InDex adapts VLA models to high-DoF dexterous manipulation via intent-conditioned fine-tuning and a decoupled diffusion head, outperforming monolithic baselines in simulation tasks with minimal data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09314","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation","primary_cat":"cs.RO","submitted_at":"2026-06-08T10:19:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"KPGrasp is a scalable Transformer flow-matching model using 3D hand keypoints that achieves 76.3% success on Dexonomy (47.4% improvement) and best average on DexGrasp Anything without contact losses or test-time refinement.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07389","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Simulation-Driven Imitation Learning for Biosignals-Free Shared-Autonomy Prosthetic Grasping","primary_cat":"cs.RO","submitted_at":"2026-06-05T15:26:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A simulation framework generates diverse reach-to-grasp demonstrations to train imitation learning policies for biosignals-free prosthetic grasping, achieving over 90% success in sim-to-real transfer.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.30569","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Any-ttach: Quick End-effector Swapping Enables Manipulation Dexterity with Simplicity","primary_cat":"cs.RO","submitted_at":"2026-05-28T21:00:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Any-ttach shows that rapid end-effector swapping combined with demonstration collection and task planning enables reliable multi-tool skills in long-horizon tasks such as sandwich making.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17354","ref_index":55,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GeoHand: Unlocking Prior Geometry Knowledge for Monocular 3D Hand Reconstruction","primary_cat":"cs.CV","submitted_at":"2026-05-17T09:45:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GeoHand adapts priors from a general-scene geometry estimator via a GeoAdapter, gated fusion, and keypoint-queried refiner to reach SOTA monocular 3D hand reconstruction on FreiHAND, DexYCB, and HO3Dv3 under heavy occlusion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13925","ref_index":205,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Towards Robotic Dexterous Hand Intelligence: A Survey","primary_cat":"cs.RO","submitted_at":"2026-05-13T15:23:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A structured survey of dexterous robotic hand research that reviews hardware, control methods, data resources, and benchmarks while identifying major limitations and future directions.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"centric datasets, the most common criteria concern physical plausibility, including penetration, analytic or quasi-static sta- bility, and diversity over generated grasp proposals or object geometries. Representative examples include DexGraspNet, GenDexGrasp, CeDex, BODex, and DexGrasp, which empha- size whether synthesized grasps are physically feasible and sufficiently diverse before policy deployment [205], [204], [194], [193], [192]. Related benchmarks such as DexTOG further connect grasp-level quality with downstream utility by jointly considering grasp validity and policy performance, explicitly distinguishing analytic grasp quality from executable manipulation value [196]. A second evaluation dimension concerns whether policies trained on a dataset can reliably execute manipulation tasks"},{"citing_arxiv_id":"2605.13117","ref_index":37,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SECOND-Grasp: Semantic Contact-guided Dexterous Grasping","primary_cat":"cs.RO","submitted_at":"2026-05-13T07:37:00+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SECOND-Grasp integrates semantic contact proposals from vision-language reasoning with geometric refinement to achieve 98%+ lifting success and improved intent-aware grasping on seen and unseen objects.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.27557","ref_index":20,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Function-based Parametric Co-Design Optimization of Dexterous Hands","primary_cat":"cs.RO","submitted_at":"2026-04-30T08:06:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A unified parametric framework optimizes dexterous hand designs by combining structure, kinematics, and fine surface geometry for grasp stability in simulation and real-world tests.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06589","ref_index":31,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes","primary_cat":"cs.RO","submitted_at":"2026-04-08T02:17:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"BiDexGrasp supplies a 9.7-million-grasp bimanual dexterous dataset built via two-stage synthesis and a coordinated geometry-size-adaptive model that generates grasps for unseen objects.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.16712","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation","primary_cat":"cs.RO","submitted_at":"2026-02-18T18:59:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A unified parameter space and canonical URDF enable cross-embodiment dexterous grasping policies with 81.9% zero-shot success on unseen hands like the 3-finger LEAP Hand.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.23087","ref_index":19,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"CoLA-Flow Policy: Temporally Coherent Imitation Learning via Continuous Latent Action Flow Matching for Robotic Manipulation","primary_cat":"cs.RO","submitted_at":"2026-01-30T15:36:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"CoLA-Flow Policy encodes action sequences into a continuous latent space and learns an explicit flow there, yielding near-single-step inference with up to 93.7% smoother trajectories and 25-point higher task success than raw-action flow baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.15953","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation","primary_cat":"cs.RO","submitted_at":"2025-06-19T01:38:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ViTacFormer learns a cross-modal visuo-tactile latent space with autoregressive tactile prediction and an easy-to-hard curriculum, then uses the representation for imitation learning that yields ~50% higher success and the first reported 11-stage, 2.5-minute autonomous dexterous tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}