{"total":51,"items":[{"citing_arxiv_id":"2605.13119","ref_index":23,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Towards Long-horizon Embodied Agents with Tool-Aligned Vision-Language-Action Models","primary_cat":"cs.RO","submitted_at":"2026-05-13T07:40:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"VLAs-as-Tools pairs a VLM planner with specialized VLA executors via a new interface and Tool-Aligned Post-Training to raise long-horizon robot success rates on LIBERO-Long and RoboTwin benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12416","ref_index":7,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Aligning Flow Map Policies with Optimal Q-Guidance","primary_cat":"cs.LG","submitted_at":"2026-05-12T17:12:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Flow map policies enable fast one-step inference for flow-based RL policies, and FMQ provides an optimal closed-form Q-guided target for offline-to-online adaptation under trust-region constraints, achieving SOTA performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12334","ref_index":7,"ref_count":1,"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 lets VLAs learn new tasks in zero-shot imagination by using a world model pre-trained only on task-free behaviors and an unmodified VLM to supply rewards, with dual-noise verification to limit hallucinations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12090","ref_index":132,"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":"Robot-centric Teleoperation QT-Opt [112], MIME [ 113], RoboNet [114], Robo T urk-Real [115], BridgeData [116], MT-Opt [117] BC-Z [118], RT-1 [119], Language-Table [120], BridgeData v2 [ 121], Jaco Play [ 122] Cable Routing Dataset [ 123], RH20T [124], OXE [125], DROID [126], RH20T-P [127], RoboMIND [128] ARIO [129], RoboData [130], DexCap [131], FuSe [132], AgiBot World [133], REASSEMBLE [ 134] OmniAction [135], UnifoLM-WBT [136] UMI-style Human Demonstration UMI [137], FastUMI [138], FastUMI-100K [139], RealOmin [140], Hoi! 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pretraining on heterogeneous robot datasets that include action-free video and producing more generalizable policies than imitation learning alone.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.19645","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success","primary_cat":"cs.RO","submitted_at":"2025-02-27T00:30:29+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"OpenVLA-OFT fine-tuning boosts LIBERO success rate from 76.5% to 97.1%, speeds action generation 26x, and outperforms baselines on real bimanual dexterous tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.05855","ref_index":22,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control","primary_cat":"cs.RO","submitted_at":"2025-02-09T11:25:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DexVLA combines a scaled diffusion action expert with embodiment curriculum learning to achieve better generalization and performance than prior VLA models on diverse robot hardware and long-horizon tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2501.09747","ref_index":52,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"FAST: Efficient Action Tokenization for Vision-Language-Action Models","primary_cat":"cs.RO","submitted_at":"2025-01-16T18:57:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"FAST applies discrete cosine transform to robot action sequences for efficient tokenization, enabling autoregressive VLAs to succeed on high-frequency dexterous tasks and scale to 10k hours of data while matching diffusion VLA performance with up to 5x faster training.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2412.14803","ref_index":116,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations","primary_cat":"cs.CV","submitted_at":"2024-12-19T12:48:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Video Prediction Policy conditions robot action learning on future-frame predictions inside fine-tuned video diffusion models, yielding 18.6% relative gains on Calvin ABC-D and 31.6% higher real-world success rates.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2411.19650","ref_index":48,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation","primary_cat":"cs.RO","submitted_at":"2024-11-29T12:06:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CogACT is a new VLA model that uses a conditioned diffusion action transformer to achieve over 35% higher average success rates than OpenVLA in simulation and 55% in real-robot experiments while generalizing to new robots and objects.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2410.24164","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","primary_cat":"cs.LG","submitted_at":"2024-10-31T17:22:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"π₀ is a vision-language-action flow model trained on diverse multi-platform robot data that supports zero-shot task performance, language instruction following, and efficient fine-tuning for dexterous tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2410.06158","ref_index":38,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation","primary_cat":"cs.RO","submitted_at":"2024-10-08T16:00:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GR-2 pre-trains on web-scale videos then fine-tunes on robot data to reach 97.7% average success across over 100 manipulation tasks with strong generalization to new scenes and objects.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2406.09246","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"OpenVLA: An Open-Source Vision-Language-Action Model","primary_cat":"cs.RO","submitted_at":"2024-06-13T15:46:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"OpenVLA achieves 16.5% higher task success than the 55B RT-2-X model across 29 tasks with 7x fewer parameters while enabling effective fine-tuning and quantization without performance loss.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"Closed-Loop Robot Control Policy User: Wipe the table. OpenVLA: [x, , Grip] = …ΔΔθΔ Multi-Robot Control & Efficient Fine-Tuning Large-Scale Robot Training Data Fully Data Weights Code Open-Source Figure 1: We present OpenVLA, a 7B-parameter open-source vision-language-action model (VLA), trained on 970k robot episodes from the Open X-Embodiment dataset [ 1]. OpenVLA sets a new state of the art for generalist robot manipulation policies. It supports controlling multiple robots out of the box and can be quickly adapted to new robot domains via parameter-efficient fine-tuning. The OpenVLA checkpoints and PyTorch training pipeline are fully open-source and models can be downloaded and fine-tuned from HuggingFace."},{"citing_arxiv_id":"2406.02523","ref_index":5,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots","primary_cat":"cs.RO","submitted_at":"2024-06-04T17:41:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RoboCasa supplies a large-scale kitchen simulator, generative assets, 100 tasks, and automated data pipelines that produce a clear scaling trend in imitation learning for generalist robots.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2405.12213","ref_index":68,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Octo: An Open-Source Generalist Robot Policy","primary_cat":"cs.RO","submitted_at":"2024-05-20T17:57:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Octo is an open-source transformer-based generalist robot policy pretrained on 800k trajectories that serves as an effective initialization for finetuning across diverse robotic platforms.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"ghosh, homer_walke, pertsch, kvablack, oier.mees}@berkeley.edu arXiv:2405.12213v2 [cs.RO] 26 May 2024 experience from other robots and tasks offers a possible solution, exposing models to a diverse set of robotic control problems that may improve generalization and performance on downstream tasks. However, even as general-purpose models become ubiquitous in natural language [ 68, 88]) and computer vision [76, 44], it has proven challenging to build the analogous \"general-purpose robot model\" that can control many robots for many tasks. Training a unified control policy in robotics presents unique challenges, requiring handling different robot embodiments, sensor setups, action spaces, task specifications, environments, and compute budgets."},{"citing_arxiv_id":"2405.05941","ref_index":11,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Evaluating Real-World Robot Manipulation Policies in Simulation","primary_cat":"cs.RO","submitted_at":"2024-05-09T17:30:16+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SIMPLER simulated environments yield policy performance that correlates strongly with real-world robot manipulation results and captures similar sensitivity to distribution shifts.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2403.12945","ref_index":39,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset","primary_cat":"cs.RO","submitted_at":"2024-03-19T17:48:38+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DROID is a new 76k-trajectory in-the-wild robot manipulation dataset spanning 564 scenes and 84 tasks that improves policy performance and generalization when used for training.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2403.09631","ref_index":44,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"3D-VLA: A 3D Vision-Language-Action Generative World Model","primary_cat":"cs.CV","submitted_at":"2024-03-14T17:58:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"3D-VLA is a new embodied foundation model that uses a 3D LLM plus aligned diffusion models to generate future images and point clouds for improved reasoning and action planning in 3D environments.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2402.12289","ref_index":26,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models","primary_cat":"cs.CV","submitted_at":"2024-02-19T17:04:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DriveVLM adds vision-language models with scene description, analysis, and hierarchical planning modules to autonomous driving, paired with a hybrid DriveVLM-Dual system tested on nuScenes and SUP-AD datasets and deployed on a production vehicle.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}