{"total":16,"items":[{"citing_arxiv_id":"2607.00678","ref_index":45,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ABot-M0.5: Unified Mobility-and-Manipulation World Action Model","primary_cat":"cs.CV","submitted_at":"2026-07-01T09:21:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ABot-M0.5 proposes a unified mobility-and-manipulation world action model using three alignment strategies that achieves state-of-the-art performance on mobile and fine-grained manipulation benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.21572","ref_index":63,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Robot Critics that Sweat the Small Stuff","primary_cat":"cs.RO","submitted_at":"2026-06-19T16:14:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Fine-tuning VLMs with pairwise progress supervision from policy rollouts improves fine-grained failure detection and boosts robot manipulation success by 11% real-world and 5.9% in simulation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20999","ref_index":58,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Inductive Generalization for Robotic Manipulation","primary_cat":"cs.RO","submitted_at":"2026-06-19T00:19:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"The paper introduces an inductive generalization evaluation protocol for manipulation policies and shows that SOTA vision-language-action models fail on progressively harder task variants.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20118","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Pose6DAug: Physically Plausible Multi-view Object Swapping for Robot Data Augmentation","primary_cat":"cs.RO","submitted_at":"2026-06-18T11:41:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Pose6DAug performs 3D multi-view object swapping via temporally coherent 6D pose trajectories to augment VLA data, reporting 16.5% relative success improvement on novel objects.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.19980","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ENPIRE: Agentic Robot Policy Self-Improvement in the Real World","primary_cat":"cs.AI","submitted_at":"2026-06-18T09:21:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"ENPIRE supplies four modules (Environment, Policy Improvement, Rollout, Evolution) that turn real-world robot training into an autonomous optimization loop driven by coding agents.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17046","ref_index":49,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Geometric Action Model for Robot Policy Learning","primary_cat":"cs.RO","submitted_at":"2026-06-15T17:58:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GAM splits a geometric foundation model to enable language-conditioned future geometry prediction and action decoding for robot policies, claiming superior performance on manipulation benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.13578","ref_index":26,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories","primary_cat":"cs.CL","submitted_at":"2026-06-11T17:03:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LabVLA uses RoboGenesis simulation data and a two-stage FAST pretraining plus flow matching recipe on a Qwen3-VL backbone to achieve the highest success rates on LabUtopia under in- and out-of-distribution conditions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12207","ref_index":39,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Intelligent Automation for Embodied Benchmark Construction: Pipelines, Embodiments, Simulators, and Trends","primary_cat":"cs.RO","submitted_at":"2026-06-10T15:25:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Automation in embodied benchmark construction shifts costs from acquisition toward validation, auditability, version control, and long-term governance instead of simply lowering total cost.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10366","ref_index":31,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Practical Recipe Towards Improving Sim-and-Real Correlation for VLA Evaluation","primary_cat":"cs.RO","submitted_at":"2026-06-09T03:25:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Authors perform a cross-simulator, cross-policy empirical study of sim-to-real correlation for VLA policies and distill guidance on using simulation for policy improvement.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06556","ref_index":87,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Robots Need More than VLA and World Models","primary_cat":"cs.RO","submitted_at":"2026-06-04T10:43:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.04811","ref_index":47,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Dream.exe: Can Video Generation Models Dream Executable Robot Manipulation?","primary_cat":"cs.CV","submitted_at":"2026-06-03T12:35:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Dream.exe evaluates 8 video generation models on 101 manipulation tasks by converting generated videos into executable robot trajectories in a simulator, finding measurable success rates that visual metrics do not predict.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17077","ref_index":28,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"How to Instruct Your Robot: Dense Language Annotations Power Robot Policy Learning","primary_cat":"cs.RO","submitted_at":"2026-05-16T16:52:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DeMiAn re-annotates robot and egocentric videos with VLM-generated dense labels across motion, scene, pose, and reasoning aspects, then uses a learned instructor to boost policy success by 5 points on RoboCasa over task-only baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12386","ref_index":14,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SafeManip: A Property-Driven Benchmark for Temporal Safety Evaluation in Robotic Manipulation","primary_cat":"cs.RO","submitted_at":"2026-05-12T16:49:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"SafeManip is a benchmark applying reusable LTLf templates across eight safety categories to evaluate temporal properties in robotic manipulation on VLA policies.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"onset, mechanism recovery, object containment, and enclosure access. Each template can be instanti- ated with task-specific objects, fixtures, regions, or skills, allowing the same safety specifications to generalize across tasks and environments. We instantiate SAFEMANIPusing 50 RoboCasa365 tasks spanning diverse manipulation and naviga- tion skills, including cleaning, cooking, and other household tasks [14]. Our evaluation covers six VLA policies and training variants, including π0 [2], π0.5 [7], GR00T N1.5 [1], and GR00T variants trained from RoboCasa365 checkpoints. For each policy rollout, SAFEMANIPjointly measures task completion and temporal safety, distinguishing successful rollouts that satisfy safety properties from those that complete the task while violating them."},{"citing_arxiv_id":"2605.07594","ref_index":22,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MemCompiler: Compile, Don't Inject -- State-Conditioned Memory for Embodied Agents","primary_cat":"cs.RO","submitted_at":"2026-05-08T11:07:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"MemCompiler reframes memory use as state-conditioned compilation, delivering relevant guidance via text and latent channels to improve embodied agent performance up to 129% and cut latency 60% versus static injection.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation. InConference on Robot Learning, pages 80-93. PMLR, 2023. [21] Dongyoung Kim, Sumin Park, Huiwon Jang, Jinwoo Shin, Jaehyung Kim, and Younggyo Seo. Robot-r1: Reinforcement learning for enhanced embodied reasoning in robotics.arXiv preprint arXiv:2506.00070, 2025. [22] Soroush Nasiriany, Sepehr Nasiriany, Abhiram Maddukuri, and Yuke Zhu. Robocasa365: A large-scale simulation framework for training and benchmarking generalist robots.arXiv preprint arXiv:2603.04356, 2026. [23] Sanjana Srivastava, Chengshu Li, Michael Lingelbach, Roberto Martín-Martín, Fei Xia, Kent Elliott Vainio, Zheng Lian, Cem Gokmen, Shyamal Buch, Karen Liu, et al."},{"citing_arxiv_id":"2605.03269","ref_index":81,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"RLDX-1 Technical Report","primary_cat":"cs.RO","submitted_at":"2026-05-05T01:40:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"RLDX-1 outperforms frontier VLAs such as π0.5 and GR00T N1.6 on dexterous manipulation benchmarks, reaching 86.8% success on ALLEX humanoid tasks versus around 40% for the baselines.","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"Steering your generalists: Improving robotic foundation models via value guidance. InConference on Robot Learning, 2024. [80] Soroush Nasiriany, Abhiram Maddukuri, Lance Zhang, Adeet Parikh, Aaron Lo, Abhishek Joshi, Ajay Mandlekar, and Yuke Zhu. Robocasa: Large-scale simulation of everyday tasks for generalist robots. InRobotics: Science and Systems, 2024. [81] Soroush Nasiriany, Sepehr Nasiriany, Abhiram Maddukuri, and Yuke Zhu. RoboCasa365: A large-scale simulation framework for training and benchmarking generalist robots.arXiv preprint arXiv:2603.04356, 2026. [82] NVIDIA. Cosmos-predict2: World simulation model for physical ai. https://github.com/nvidia-cosmos/ cosmos-predict2, 2025. [83] NVIDIA. Nsight Compute."},{"citing_arxiv_id":"2604.22748","ref_index":276,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond","primary_cat":"cs.AI","submitted_at":"2026-04-24T17:48:47+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}