{"total":19,"items":[{"citing_arxiv_id":"2607.02431","ref_index":30,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"WorldSample: Closed-loop Real-robot RL with World Modelling","primary_cat":"cs.RO","submitted_at":"2026-07-02T17:00:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"WorldSample generates synthetic transitions from a post-trained world model grounded in real rollouts and uses Policy-Paced Learning to improve RL policies, reporting 28% higher success rates and 59% fewer training steps on contact-rich robot tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2607.01060","ref_index":37,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation","primary_cat":"cs.RO","submitted_at":"2026-07-01T15:22:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RoboWorld introduces an automated pipeline using autoregressive video world models and task-progress VLM scoring, plus Step Forcing for long-horizon stability, to achieve high correlation with real robot policy evaluation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.32028","ref_index":45,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"DVG-WM: Disentangled Video Generation Enables Efficient Embodied World Model for Robotic Manipulation","primary_cat":"cs.RO","submitted_at":"2026-06-30T17:54:32+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},{"citing_arxiv_id":"2606.29834","ref_index":6,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning","primary_cat":"cs.RO","submitted_at":"2026-06-29T06:19:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"STEAM learns advantages from expert trajectories via self-supervised temporal ensemble modeling to improve policy learning on real robot tasks like bimanual folding and pick-and-place.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.15032","ref_index":65,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"How Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position","primary_cat":"cs.LG","submitted_at":"2026-06-13T00:21:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The paper proposes an L0-L7 evidential ladder for evaluating world models in embodied decision-making, prioritizing interventional action fidelity and policy optimization utility over visual plausibility.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12403","ref_index":33,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"World Pilot: Steering Vision-Language-Action Models with World-Action Priors","primary_cat":"cs.RO","submitted_at":"2026-06-10T17:59:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"World Pilot augments VLA policies with world-action priors through latent and action steering pathways, reporting 84.7% success on LIBERO-Plus zero-shot OOD and top real-robot results across four tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09615","ref_index":40,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"DexPIE: Stable Dexterous Policy Improvement from Real-World Experience","primary_cat":"cs.RO","submitted_at":"2026-06-08T15:21:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"DexPIE improves dexterous manipulation success rates by 37% over demo policies via real-world experience collection with adapted intervention, multi-stage DAgger, asynchronous relative-action inference, and optimality conditioning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09499","ref_index":15,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Targeting World Models to Compromise Robot Learning Pipelines","primary_cat":"cs.RO","submitted_at":"2026-06-08T13:50:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"World models introduce a stealthy poisoning vector into robot learning pipelines where malicious prompts or dynamics in teleoperated data activate only during synthetic trajectory generation, enabling backdoors in downstream policies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.05645","ref_index":88,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning","primary_cat":"cs.RO","submitted_at":"2026-06-04T03:16:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Discrete-WAM unifies world modeling and policy learning for autonomous driving by representing observations, states, decisions, and actions as tokens in one space and using hierarchical token editing for planning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.03240","ref_index":12,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"GeoAlign: Beyond Semantics with State-Guided Spatial Alignment in VLA Models","primary_cat":"cs.RO","submitted_at":"2026-06-02T07:01:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"GeoAlign post-trains an RGB geometry branch on robot RGB-D data to produce GEP features that are queried by proprioceptive state to generate phase-dependent geometry tokens, yielding 99.0% on LIBERO, 85.3% on SimplerEnv-Fractal, and 78.8% on real ALOHA tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00113","ref_index":98,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"World Models for Robotic Manipulation: A Survey","primary_cat":"cs.RO","submitted_at":"2026-05-27T05:32:17+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Survey organizing world models for robotic manipulation into representation families, a functional taxonomy, and infrastructure roles across pretraining, post-training, and inference, while reviewing 34 datasets and evaluation protocols.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27947","ref_index":24,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"SANTS: A State-Adaptive Scheduler for World Action Models","primary_cat":"cs.RO","submitted_at":"2026-05-27T04:40:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SANTS adaptively chooses denoising depth in video-based robot action diffusion policies using a state-dependent stopping hazard and noise ratio, trained via downstream action reward to reduce latency.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16412","ref_index":30,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"SCAR: Self-Supervised Continuous Action Representation Learning","primary_cat":"cs.RO","submitted_at":"2026-05-13T16:23:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SCAR proposes a joint inverse-forward dynamics framework to learn transferable continuous action representations across embodiments from visual data using regularization and adversarial invariance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12334","ref_index":39,"ref_count":2,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Reinforcing VLAs in Task-Agnostic World Models","primary_cat":"cs.AI","submitted_at":"2026-05-12T16:16:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RAW-Dream disentangles world-model learning from task data by using a pre-trained task-agnostic world model and VLM rewards, with dual-noise filtering, to enable zero-shot VLA adaptation in simulation and real settings.","context_count":1,"top_context_role":"other","top_context_polarity":"unclear","context_text":"et al. Co-evolving latent action world models.arXiv preprint arXiv:2510.26433, 2025. [37] Xiao, J. et al. World-env: Leveraging world model as a virtual environment for vla post-training. arXiv preprint arXiv:2509.24948, 2025. [38] Xu, C. et al. Rl token: Bootstrapping online rl with vision-language-action models.arXiv preprint arXiv:2604.23073, 2026. [39] Yang, J. et al. Rise: Self-improving robot policy with compositional world model.arXiv preprint arXiv:2602.11075, 2026. [40] Yin, T. et al. Playworld: Learning robot world models from autonomous play.arXiv preprint arXiv:2603.09030, 2026. [41] Yu, C. et al. Rlinf: Flexible and efficient large-scale reinforcement learning via macro-to-micro flow transformation."},{"citing_arxiv_id":"2605.12090","ref_index":47,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"World Action Models: The Next Frontier in Embodied AI","primary_cat":"cs.RO","submitted_at":"2026-05-12T13:10:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Langugae-Conditoned MoCoGAN [29], U-Net [30], Latte [ 31], Wan [32], Sora 2 [ 33]. . . Embodied World Model SWIM [34], DreamDojo [ 35], RoboDreamer [36], RoboScape [37]. . . WM for VLA Imitation Learning Ctrl-World [38], RoboScape [37], DREMA [ 39] Reinforcement Learning Dreamer to Control [ 40] DreamerV2 [ 41], Dreamer 4 [ 42], RISE [ 43] DreamerV3 [44], DayDreamer [45], World-Env [46], RoboScape-R [47] WMPO [48], WoVR [49], VLA-RFT [50], RWML [51], MoDem-V2 [52] World-Gymnast [53], RWM-U [54], World4RL [55], VIPER [ 56] PhysWorld [57], Diffusion Reward [58], GenReward [59] Evaluation Ctrl-World [38], Veo Robotics [60], Interactive World Simulator [61] WorldEval [62], WorldGym [63], dWorldEval [64] Architecture Cascaded W AM Explicit UniPi [6], VLP [ 7], RoboEnvision [9], ThisThat [ 65], TesserAct [66], MVISTA-4D [67]"},{"citing_arxiv_id":"2605.06481","ref_index":86,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation","primary_cat":"cs.RO","submitted_at":"2026-05-07T16:06:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"OA-WAM uses persistent address vectors and dynamic content vectors in object slots to enable addressable world-action prediction, improving robustness on manipulation benchmarks under scene changes.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[84] Yuhan Xie, Yuping Yan, Yunqi Zhao, Handing Wang, and Yaochu Jin. STRONG-VLA: Decoupled robustness learning for Vision-Language-Action models under multimodal perturbations.arXiv preprint arXiv:2604.10055, 2026. [85] Fuxiang Yang, Donglin Di, Lulu Tang, Xuancheng Zhang, Lei Fan, et al. Chain of World: World model thinking in latent motion.arXiv preprint arXiv:2603.03195, 2026. [86] Jiazhi Yang, Kunyang Lin, Jinwei Li, Wencong Zhang, Tianwei Lin, Longyan Wu, Zhizhong Su, Hao Zhao, Ya-Qin Zhang, Li Chen, Ping Luo, Xiangyu Yue, and Hongyang Li. RISE: Self-improving robot policy with compositional world model.arXiv preprint arXiv:2602.11075, 2026. [87] Yandan Yang, Shuang Zeng, Tong Lin, Xinyuan Chang, Dekang Qi, et al. ABot-M0: Vla foundation"},{"citing_arxiv_id":"2605.00080","ref_index":61,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"World Model for Robot Learning: A Comprehensive Survey","primary_cat":"cs.RO","submitted_at":"2026-04-30T14:35:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"A comprehensive survey that organizes the literature on world models in robot learning, their roles in policy learning, planning, simulation, and video-based generation, with connections to navigation, driving, datasets, and benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.08544","ref_index":52,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds","primary_cat":"cs.RO","submitted_at":"2026-04-09T17:59:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SIM1 converts sparse real demonstrations into high-fidelity synthetic data through physics-aligned simulation, yielding policies that match real-data performance at a 1:15 ratio with 90% zero-shot success on deformable manipulation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.07335","ref_index":6,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"TAMEn: Tactile-Aware Manipulation Engine for Closed-Loop Data Collection in Contact-Rich Tasks","primary_cat":"cs.RO","submitted_at":"2026-04-08T17:49:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"TAMEn supplies a cross-morphology wearable interface and pyramid-structured visuo-tactile data regime that raises bimanual manipulation success rates from 34% to 75% via closed-loop collection.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[4] N. Funk, E. Helmut, G. Chalvatzaki, R. Calandra, and J. Peters, \"Evetac: An event-based optical tactile sensor for robotic manipulation,\"TRO, 2024. [5] X. Zhai, Z. Huang, L. Wu, Q. Zhao, Q. Yu, J. Ren, C. Hao, and H. Soh, \"Skillvla: Tackling combinatorial diversity in dual-arm manipulation via skill reuse,\"arXiv preprint arXiv:2603.03836, 2026. [6] J. Yang, K. Lin, J. Li, W. Zhang, T. Lin, L. Wu, Z. Su, H. Zhao, Y .-Q. Zhang, L. Chen, P. Luo, X. Yue, and H. Li, \"Rise: Self- improving robot policy with compositional world model,\"arXiv preprint arXiv:2602.11075, 2026. [7] P. Intelligence, K. Black, N. Brown, J. Darpinian, K. Dhabalia, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, M. Y . Galliker, D."}],"limit":50,"offset":0}