{"total":39,"items":[{"citing_arxiv_id":"2606.31494","ref_index":15,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Robustness of Robotic Manipulation: Foundations and Frontiers","primary_cat":"cs.RO","submitted_at":"2026-06-30T11:13:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A survey that formalizes manipulation robustness from probabilistic and control perspectives and reviews mechanisms, metrics, and open problems across robotics subfields.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.31037","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Labimus: A Simulation and Benchmark for Humanoid Dexterous Manipulation in Chemical Laboratory","primary_cat":"cs.RO","submitted_at":"2026-06-30T02:14:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Labimus is the first benchmark for humanoid dexterous manipulation in organic chemistry laboratories, exposing a gap between task completion and required experimental precision.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.29201","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Behavior Uncloning: Distilling Mode Redirection into Policy Weights without Inference-Time Steering","primary_cat":"cs.RO","submitted_at":"2026-06-28T05:01:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MoRE improves robot policy success rates by 44 percentage points by distilling mode redirection into weights, matching filtered retraining performance without inference overhead.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.22540","ref_index":41,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models","primary_cat":"cs.CV","submitted_at":"2026-06-21T14:54:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PolicyTrim is an RL post-training framework that boosts VLA policy efficiency by 3x chunk utilization and 51.4% fewer steps, yielding up to 5.83x speedup.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20999","ref_index":50,"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":"2606.20871","ref_index":37,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Geometric Entropy: When Trajectory Diversity Helps and Hurts in Imitation Learning","primary_cat":"cs.RO","submitted_at":"2026-06-18T19:02:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Geometric diversity of demonstration trajectories exhibits an inverted-U effect on imitation learning success, with the peak shifting lower as mastery increases via more data, easier tasks, or stronger priors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20781","ref_index":150,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"World Action Models: A Survey","primary_cat":"cs.RO","submitted_at":"2026-06-18T17:05:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"A survey that clarifies boundaries and organizes World Action Models by generation requirements and predictive substrates, identifying a trend toward generating less of the future.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"strengthen latent future prediction before action decoding. Internet video is therefore a scale source only after the action-label problem has been made explicit. Simulation.Simulation provides exact action labels, controlled curricula, and low marginal cost after the environment has been authored. Classical physics platforms for manipulation include Robosuite [215], ManiSkill 2 [47], ManiSkill 3 [150], MetaWorld [190], and LIBERO [105]. General simulation backends such as IsaacSim [127], CoppeliaSim [137], and SimplerEnv [95] provide the execution substrate for broader embodied tasks. Household and digital-twin datasets build on these engines to generate demonstrations programmatically, including RoboCasa [123], RoboTwin [122], and RoboTwin 2 [20]."},{"citing_arxiv_id":"2606.20092","ref_index":37,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies","primary_cat":"cs.CV","submitted_at":"2026-06-18T11:11:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"EventVLA introduces foundational visual anchors and a Keyframe Evidence Memory module that predicts future keyframe probabilities from VLA embeddings to improve long-horizon task success by an average of 40% on 17 simulation and 4 real-world tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.18646","ref_index":51,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Scalable Embodied Intelligence Platform for Seamless Real-to-Sim-to-Real Transfer of Household Mobile Manipulation Tasks","primary_cat":"cs.RO","submitted_at":"2026-06-17T03:31:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"BestMan is a robotics platform with ASG for scene reconstruction, simulation-guided skill learning, and HUM middleware to enable seamless real-to-sim-to-real transfer in household mobile manipulation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17511","ref_index":71,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MagicSim: A Unified Infrastructure for Executable Embodied Interaction","primary_cat":"cs.RO","submitted_at":"2026-06-16T04:42:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MagicSim is a unified embodied interaction infrastructure built on a deterministic batched runtime and shared MDP that supports diverse world construction, execution, task evaluation, automatic rollout generation, and interactive agent interfaces.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.13578","ref_index":31,"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":"2606.11324","ref_index":61,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models","primary_cat":"cs.RO","submitted_at":"2026-06-09T18:07:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Embodied-R1.5 is an 8B EFM achieving SOTA on 16 of 24 embodied VLM benchmarks, fine-tunable to outperform leading VLAs, with claimed zero-shot real-robot generalization.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10382","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"UMI-Bench 1.0: An Open and Reproducible Real-World Benchmark for Tabletop Robotic Manipulation with UMI Data","primary_cat":"cs.RO","submitted_at":"2026-06-09T03:47:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"UMI-Bench 1.0 is presented as the first open benchmark dedicated to reproducible real-world evaluation of Universal Manipulation Interface policies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07723","ref_index":66,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation","primary_cat":"cs.RO","submitted_at":"2026-06-05T16:21:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"VoLoAgent uses a VLM to steer heterogeneous robot capabilities as interruptible tools for long-horizon manipulation and introduces the RoboVoLo benchmark, claiming substantial outperformance over single VLA/VLM or tool-based systems with real-robot validation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.03551","ref_index":55,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"NVIDIA Isaac Sim: Enabling Scalable, GPU-Accelerated Simulation for Robotics","primary_cat":"cs.RO","submitted_at":"2026-06-02T12:12:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A survey reviewing the architecture, usage patterns, and limitations of NVIDIA Isaac Sim across robotics domains.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.30795","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Feat2Go: Visual Feature-Grounded Value Estimation for Embodied Reinforcement Learning","primary_cat":"cs.RO","submitted_at":"2026-05-29T03:36:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Feat2Go uses patch-level similarity from a visual world model and trend-based clustering to create progress targets for training value models that improve reward shaping in embodied RL for VLA policies, yielding large gains on ManiSkill3 and RoboTwin benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.29710","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"PhAIL: A Real-Robot VLA Benchmark and Distributional Methodology","primary_cat":"cs.RO","submitted_at":"2026-05-28T10:10:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"PhAIL provides an open benchmark and distributional evaluation method for real-robot VLA policies using time-to-success CDF, HRT scoring, and KS significance tests.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22882","ref_index":45,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation","primary_cat":"cs.CV","submitted_at":"2026-05-20T21:36:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GEM-4D improves video world models for robot manipulation by distilling 4D geometric correspondences into training and adding an inverse dynamics module, achieving SOTA geometric consistency and 81% real-world success.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17933","ref_index":38,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"AtlasVA: Self-Evolving Visual Skill Memory for Teacher-Free VLM Agents","primary_cat":"cs.CV","submitted_at":"2026-05-18T06:41:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"AtlasVA organizes VLM agent memory into spatial heatmaps, visual exemplars, and symbolic skills, evolving atlases from trajectories to act as potential-based shaping rewards in teacher-free reinforcement learning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17522","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"RoboFlow4D: A Lightweight Flow World Model Toward Real-Time Flow-Guided Robotic Manipulation","primary_cat":"cs.RO","submitted_at":"2026-05-17T16:11:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"RoboFlow4D is an end-to-end lightweight flow world model that predicts multi-frame 3D flows from visual observations and textual instructions to provide explicit planning for real-time robotic manipulation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.15836","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GAP: Geometric Anchor Pre-training for Data-Efficient Visuomotor Learning of Manipulation Tasks","primary_cat":"cs.RO","submitted_at":"2026-05-15T10:48:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GAP pre-trains the spatial adapter on a lightweight simulated proxy task with free object masks to generate repeatable geometric keypoints, yielding higher success rates than baselines in low-data robotic manipulation on RoboMimic and ManiSkill.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13105","ref_index":21,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models","primary_cat":"cs.RO","submitted_at":"2026-05-13T07:15:37+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PAIR-VLA adds invariance and sensitivity objectives over paired visual variants during PPO fine-tuning of VLA models, yielding 9-16% average gains on ManiSkill3 under distractors, textures, poses, viewpoints, and lighting shifts.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12090","ref_index":226,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"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":"dataset","top_context_polarity":"background","context_text":"MetaWorld [207], RLBench [ 208], Robomimic [209], Franka Kitchen [ 210], ManiSkill [ 211] ManiSkill2 [151], ManiSkill3 [ 212], RoboCasa [152], CAL VIN [213], VIMAbench [214] VLMbench [215], LIBERO [216], Libero-plus [ 4], Libero-pro [ 217], Libero-X [ 218] COLOSSEUM [219], AGNOSTOS [220], RoboEval [221], RoboVerse [222], PolaRiS [223] RoboMME [224], GenManip [ 225], VLABench [ 226], RoboSuite [227], RoboLab [228] SimplerEnv [229], ARNOLD [230], GemBench [231] Bimanual and Humanoid Form Robo T win [153], BiGym [232], HumanoidBench [ 233] HumanoidGen [234] Mobile Manipulation ManipulaTHOR [235], HomeRobot [236], BEHA VIOR-1K [237] Contact and Deformation Manipulation SoftGym [238], PlasticineLab [239], DaXBench [240] TacSL [241], ManiFeel [242]"},{"citing_arxiv_id":"2605.11665","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Nautilus: From One Prompt to Plug-and-Play Robot Learning","primary_cat":"cs.RO","submitted_at":"2026-05-12T07:26:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"NAUTILUS is a prompt-driven harness that automates plug-and-play adapters, typed contracts, and validation for policies, benchmarks, and robots in learning research.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"NAUTILUSintroduces a shared robot learning harness: a substrate of typed contracts, chambered execution, and uniform transport, plus a content layer of Guides, Sensors, and State. This changes the unit of work from pairwise integration to reusable onboarding, reducing the burden toΘ(N+M+K). (W AM)), benchmark suitesB (e.g., LIBERO [9], RoboCasa [10], ManiSkill [11], ALOHA [12], etc.), and robot embodiments R (e.g., single-arm, bimanual, dexterous-hand, locomotion, humanoid, etc.), with cardinalities N, M, and K, respectively. Each non-trivial (P, B, R) cross-comparison typically requires a distinct hand-written integration layer, so docker container setup, observation adapters, smoke tests, and trust-validation procedures are repeatedly re-implemented across papers, labs, and"},{"citing_arxiv_id":"2605.10921","ref_index":59,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark","primary_cat":"cs.RO","submitted_at":"2026-05-11T17:54:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RoboMemArena is a new large-scale robotic memory benchmark with real-world tasks, and PrediMem is a dual VLA system that outperforms baselines by managing memory buffers with predictive coding.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.03269","ref_index":99,"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":"Rt-sketch: Goal-conditioned imitation learning from hand-drawn sketches. InConference on Robot Learning, 2024. [98] Huajie Tan, Sixiang Chen, Yijie Xu, Zixiao Wang, Yuheng Ji, Cheng Chi, Yaoxu Lyu, Zhongxia Zhao, Xiansheng Chen, Peterson Co, et al. Robo-dopamine: General process reward modeling for high-precision robotic manipulation.arXiv preprint arXiv:2512.23703, 2025. [99] Stone Tao, Fanbo Xiang, Arth Shukla, Yuzhe Qin, Xander Hinrichsen, Xiaodi Yuan, Chen Bao, Xinsong Lin, Yulin Liu, Tse-kai Chan, et al. Maniskill3: Gpu parallelized robotics simulation and rendering for generalizable embodied ai. arXiv preprint arXiv:2410.00425, 2024. [100] Marcel Torne, Andy Tang, Yuejiang Liu, and Chelsea Finn. Learning long-context diffusion policies via past-token"},{"citing_arxiv_id":"2605.02487","ref_index":66,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Visibility-Aware Mobile Grasping in Dynamic Environments","primary_cat":"cs.RO","submitted_at":"2026-05-04T11:41:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A visibility-aware mobile grasping system with iterative whole-body planning and behavior-tree subgoal generation achieves 68.8% success in unknown static and 58% in dynamic environments, outperforming a baseline by 22.8% and 18%.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.26509","ref_index":10,"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":"Index Terms-3D generation, embodied AI, robotic simulation, scene generation, sim-to-real transfer ✦ 1 INTRODUCTION E MBODIEDAI and robotic systems are increasingly ex- pected to perceive, reason, and act in open-ended phys- ical environments [1], [2]. Recent progress in large-scale policy learning [3], [4], vision-language-action models [5]- [9], and high-fidelity simulation [10]-[12] has significantly expanded what these systems can do. However, their per- formance remains fundamentally constrained by the avail- ability of scalable, diverse, and interaction-ready 3D assets and environments. In contrast to conventional 3D gener- ation, which often prioritizes appearance realism or static geometry, embodied applications require assets that can be"},{"citing_arxiv_id":"2604.25126","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"HANDFUL: Sequential Grasp-Conditioned Dexterous Manipulation with Resource Awareness","primary_cat":"cs.RO","submitted_at":"2026-04-28T02:04:50+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":"2604.22363","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"LeHome: A Simulation Environment for Deformable Object Manipulation in Household Scenarios","primary_cat":"cs.RO","submitted_at":"2026-04-24T08:53:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LeHome is a simulation platform offering high-fidelity dynamics for robotic manipulation of varied deformable objects in household settings, with support for multiple robot embodiments including low-cost hardware.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.15215","ref_index":34,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics","primary_cat":"cs.RO","submitted_at":"2026-04-16T16:47:08+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":"2604.13800","ref_index":18,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"EmbodiedClaw: Conversational Workflow Execution for Embodied AI Development","primary_cat":"cs.RO","submitted_at":"2026-04-15T12:36:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"EmbodiedClaw automates embodied AI development workflows through conversation, reducing manual effort and improving consistency and reproducibility.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.10982","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"{\\Psi}-Map: Panoptic Surface Integrated Mapping Enables Real2Sim Transfer","primary_cat":"cs.RO","submitted_at":"2026-04-13T04:41:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Ψ-Map combines plane-constrained Gaussian surfels from LiDAR with end-to-end panoptic lifting to deliver high-precision geometric and semantic reconstruction in large-scale environments at real-time speeds.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.05831","ref_index":52,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"BiCoord: A Bimanual Manipulation Benchmark towards Long-Horizon Spatial-Temporal Coordination","primary_cat":"cs.RO","submitted_at":"2026-04-07T13:02:17+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"BiCoord is a new benchmark for long-horizon tightly coordinated bimanual manipulation that includes quantitative metrics and shows existing policies like DP, RDT, Pi0 and OpenVLA-OFT struggle on such tasks.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"for model-based control. InIEEE/RSJ International Conference on Intelligent Robots and Systems. 5026-5033. [51] Homer Rich Walke, Kevin Black, Tony Z Zhao, Quan Vuong, Chongyi Zheng, Philippe Hansen-Estruch, Andre Wang He, Vivek Myers, Moo Jin Kim, Max Du, et al. 2023. Bridgedata v2: A dataset for robot learning at scale. InConference on Robot Learning. PMLR, 1723-1736. [52] Chenxi Wang, Hongjie Fang, Hao-Shu Fang, and Cewu Lu. 2024. Rise: 3d per- ception makes real-world robot imitation simple and effective. In2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2870- 2877. [53] Dian Wang, Colin Kohler, Xupeng Zhu, Mingxi Jia, and Robert Platt. 2022. Bul- letarm: An open-source robotic manipulation benchmark and learning frame-"},{"citing_arxiv_id":"2604.05484","ref_index":52,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"CoEnv: Driving Embodied Multi-Agent Collaboration via Compositional Environment","primary_cat":"cs.RO","submitted_at":"2026-04-07T06:24:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"CoEnv introduces a compositional environment that integrates real and simulated spaces for multi-agent robotic collaboration, using real-to-sim reconstruction, VLM action synthesis, and validated sim-to-real transfer to achieve high success rates on multi-arm manipulation tasks.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"costly collisions, while the digital world enables cost-effective and safe testing. CoEnv composes both worlds through pose and action alignment, forming acompositional en- vironment(left) that supports real-to-sim reconstruction, simulation-conditioned ac- tion synthesis, and safe real-world deployment (right). single-agent systems have achieved remarkable progress [7,52], complex long- horizon manipulation scenarios increasingly demand the coordination of mul- tiple embodied agents, whose complementary capabilities enable more efficient and robust task completion than any individual agent alone. Multi-agent embodied systems inherently possess greater capability to handle sophisticated tasks through parallel execution and role specialization."},{"citing_arxiv_id":"2604.04539","ref_index":81,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control","primary_cat":"cs.LG","submitted_at":"2026-04-06T09:03:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"FlashSAC improves training speed and final performance of off-policy RL on high-dimensional robot tasks by reducing update frequency, increasing model scale, and bounding norms to limit critic error accumulation.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"results in robotic control [33, 83], reinforcement learning (RL) from simulation remains a core paradigm when expert demonstrations are unavailable, incomplete, or insufficient [34, 3, 95]. To date, sim-to-real RL has been most successful in relatively constrained domains such as quadruped locomotion [27,68] and gripper-based manipulation [2], which are characterized by low-dimensional state-action spaces and extremely high-throughput simulators [81, 54, 4, 93]. In this regime, on-policy methods such as Proximal Policy Optimization (PPO) [72, 73] have proven effective: PPO is stable, easy to tune, and its data inefficiency is acceptable when fresh on-policy data can be collected cheaply. However, thisregimeisbecominglessrepresentativeofmodernrobotlearning. Emergingapplications-including humanoid locomotion [76], dexterous manipulation [10, 90], and vision-based control [59, 31]-involve much"},{"citing_arxiv_id":"2602.03433","ref_index":208,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"When control meets large language models: From words to dynamics","primary_cat":"eess.SY","submitted_at":"2026-02-03T11:56:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"The paper proposes a bidirectional continuum between LLMs and control systems, covering LLM-assisted controller design, control-based LLM steering, and state-space modeling of LLMs.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"collision avoidance, & regulatory compliance Evaluated on benchmarks; no real-time integration •Navigation:Modelsthatinterpretlanguageorvisualcues to guide autonomous navigation, locomotion, or human- robot interaction [169, 205, 206, 207]. •Simulation frameworks and testbeds:Platforms that enable LLM-driven experimentation, benchmarking, and RL in realistic or large-scale simulated areas [208]. •Safety, risk mitigation, and adversarial testing:Re- searchaddressingrobustness,bias,formalguarantees,and red-teaming of LLM-driven robotic systems [209, 210, 211, 212, 213, 214, 215]. This taxonomy highlights how robotics has become the leading testbed for embodied LLM control, offering numer- ous concrete implementations in direct policy generation,"},{"citing_arxiv_id":"2508.13998","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation","primary_cat":"cs.RO","submitted_at":"2025-08-19T16:50:01+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Embodied-R1 uses a pointing-centric representation and reinforced fine-tuning on a 200K dataset to achieve state-of-the-art results on embodied benchmarks plus 56.2% success in SIMPLEREnv and 87.5% on real XArm tasks without task-specific training.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2507.05331","ref_index":44,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation","primary_cat":"cs.RO","submitted_at":"2025-07-07T17:56:01+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Multi-task pretraining of diffusion policies on diverse robot data produces more successful, robust, and data-efficient policies for dexterous manipulation than single-task baselines, with performance scaling with pretraining size and diversity.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}