{"total":13,"items":[{"citing_arxiv_id":"2605.23204","ref_index":145,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery","primary_cat":"cs.AI","submitted_at":"2026-05-22T03:40:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A survey organizing AI-powered research automation into five workflow stages, defining AutoResearch and Vibe Research, and proposing five evaluation dimensions while noting domain-conditioned limits on autonomy.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"creates semantically and geometrically analogous simulated environments for robust policy learning [143]. R2R2R, RoboGSim, Re3Sim, and ReBot all reduce dependence on manual teleoperation or expensive hardware collection by reconstructing or replaying real scenes into scalable simulation pipelines, then using those pipelines for data generation, training, or evaluation [144, 145, 146, 147]. This makes embodied intelligence one of the clearest domains where AutoResearch already functions as a research-engineering flywheel: real data seeds simulation, simulation scales data, and scaled data feeds model improvement. • Autonomy boundary.The present autonomy ceiling in embodied intelligence is best described as selective workflow autonomy rather than robust scientific autonomy."},{"citing_arxiv_id":"2605.19986","ref_index":38,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Beyond Binary Success: A Diagnostic Meta-Evaluation Framework for Fine-Grained Manipulation","primary_cat":"cs.RO","submitted_at":"2026-05-19T15:25:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"MetaFine reconstructs benchmarks into diagnostic scenarios to evaluate vision-language-action models on fine-grained manipulation, exposing dimension-specific failures and identifying the visual encoder as a key bottleneck.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.01232","ref_index":18,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Principled Approach for Creating High-fidelity Synthetic Demonstrations for Imitation Learning","primary_cat":"cs.RO","submitted_at":"2026-05-02T04:09:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DMP retargeting within 3DGS scenes preserves expert motion shape and phase to create diverse yet high-fidelity demonstrations, yielding lower deviation, fewer collisions, and higher downstream policy success than planner-based synthesis on Spot manipulator tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.26509","ref_index":62,"ref_count":3,"confidence":0.9,"is_internal_anchor":false,"paper_title":"3D Generation for Embodied AI and Robotic Simulation: A Survey","primary_cat":"cs.RO","submitted_at":"2026-04-29T10:17:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"The paper surveys 3D generation techniques for embodied AI and robotics, categorizing them into data generation, simulation environments, and sim-to-real bridging while identifying bottlenecks in physical validity and transfer.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"canonicalization networks [61].Real-to-Sim (R2S) reconstruc- tionbuilds high-fidelity digital replicas of real environments to reduce the domain gap at its source.Real-to-Sim-to-Real (R2S2R) pipelinescombine both directions: reconstructing the real world into simulation, generating or augmenting data within it, and transferring the resulting policies back to reality [62], [63]. World Models.World models[64] learn to simulate envi- ronment dynamics from experience, enabling large-scale imaginary rollouts for long-horizon planning without real- world cost. Recent methods leverage action-conditioned video diffusion [65], [66] or 3D scene generation to serve as neural simulators. Vision-Language-Action Models.Vision-Language-Action"},{"citing_arxiv_id":"2604.25459","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning","primary_cat":"cs.RO","submitted_at":"2026-04-28T10:05:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GS-Playground delivers a high-throughput photorealistic simulator for vision-informed robot learning via parallel physics integrated with batch 3D Gaussian Splatting at 10^4 FPS and an automated Real2Sim workflow for consistent environments.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"in terms of reconstruction paradigms [48, 38, 22], model efficiency [15, 12, 14], and generative Gaussian models [8, 31]. However, these methods generally struggle to directly meet the requirements of robotic simulators for sim-ready scenes. Existing works indicate that 3DGS-based rendering holds significant potential in robotics, enhancing sim-to-real transfer for vision-based policies [29, 13, 40], augmenting training datasets [3, 33, 54, 52], and supporting real-to-sim evaluation pipelines [21, 1, 20, 59]. While GaussGym [11] pioneered the application of 3DGS in RL, our work extends this capability to contact-rich manipulation and larger-scale parallel throughput, providing a more versatile foundation for diverse vision- informed robot learning tasks."},{"citing_arxiv_id":"2604.15805","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"From Seeing to Simulating: Generative High-Fidelity Simulation with Digital Cousins for Generalizable Robot Learning and Evaluation","primary_cat":"cs.RO","submitted_at":"2026-04-17T08:06:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Digital Cousins is a generative real-to-sim method that creates diverse high-fidelity simulation scenes from real panoramas to improve generalization in robot learning and evaluation.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"arXiv:2108.03272, 2021. [28] Chengshu Li, Ruohan Zhang, Josiah Wong, Cem Gok- men, Sanjana Srivastava, Roberto Mart 'ın-Mart'ın, Chen Wang, Gabrael Levine, Michael Lingelbach, Jiankai Sun, et al. Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation. In Conference on Robot Learning, pages 80-93. PMLR, 2023. [29] Xinhai Li, Jialin Li, Ziheng Zhang, Rui Zhang, Fan Jia, Tiancai Wang, Haoqiang Fan, Kuo-Kun Tseng, and Ruiping Wang. Robogsim: A real2sim2real robotic gaussian splatting simulator, 2025. URL https://arxiv. org/abs/2411.11839. [30] Zeyi Li, Jade Yang, Jingkai Xu, Shangbin Xie, Yuran Wang, Zhenhao Shen, Tianxing Chen, Yan Shen, Wenjun Li, Yukun Zheng, Chaorui Zhang, Ming Chen, Chen Xie,"},{"citing_arxiv_id":"2604.11138","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation","primary_cat":"cs.RO","submitted_at":"2026-04-13T07:50:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A framework using 3D Gaussian Splatting for visual domain randomization enables robust monocular RGB-based dexterous in-hand reorientation on real hardware for multiple objects under varied lighting.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.07728","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GEAR: GEometry-motion Alternating Refinement for Articulated Object Modeling with Gaussian Splatting","primary_cat":"cs.CV","submitted_at":"2026-04-09T02:24:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"GEAR is an EM-style alternating optimization framework that jointly models geometry and motion in Gaussian Splatting to improve reconstruction of complex articulated objects.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.07105","ref_index":21,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Genie Sim PanoRecon: Fast Immersive Scene Generation from Single-View Panorama","primary_cat":"cs.RO","submitted_at":"2026-04-08T13:57:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A feed-forward Gaussian-splatting system reconstructs photo-realistic 3D scenes from single-view panoramas in seconds via cube-map decomposition and depth-aware fusion for robotic simulation use.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.07866","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Viewpoint-Agnostic Grasp Pipeline using VLM and Partial Observations","primary_cat":"cs.RO","submitted_at":"2026-03-09T00:42:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The viewpoint-agnostic grasp pipeline using VLM and partial observation handling achieves 90% success (9/10 trials) in cluttered tabletop scenarios on a real quadruped robot, outperforming a view-dependent baseline at 30% (3/10) through open-vocabulary detection, point cloud completion, and safety-0","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.09023","ref_index":30,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation","primary_cat":"cs.RO","submitted_at":"2026-02-09T18:59:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"TwinRL expands RL exploration via digital twin reconstruction and twin RL warm-up to guide real-world learning, reaching near-100% success with 20 minutes of on-robot time across four tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.00678","ref_index":48,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Toward Reliable Sim-to-Real Predictability for MoE-based Robust Quadrupedal Locomotion","primary_cat":"cs.RO","submitted_at":"2026-01-31T11:50:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MoE-based locomotion policy with RoboGauge metrics achieves reliable sim-to-real transfer, enabling robust quadrupedal walking on challenging unseen terrains up to 4 m/s.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.01773","ref_index":41,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"IGen: Scalable Data Generation for Robot Learning from Open-World Images","primary_cat":"cs.RO","submitted_at":"2025-12-01T15:15:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"IGen generates realistic visuomotor training data including actions and temporally coherent visuals from unstructured open-world images via 3D reconstruction and VLM reasoning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}