{"total":69,"rows":[{"id":"2512.13030","title":"Motus: A Unified Latent Action World Model","created":"2025-12-15","cited_by_count":251,"area":"wm"},{"id":"2505.12705","title":"DreamGen: Unlocking Generalization in Robot Learning through Video World Models","created":"2025-05-19","cited_by_count":179,"area":"wm"},{"id":"2505.15659","title":"FLARE: Robot Learning with Implicit World Modeling","created":"2025-05-21","cited_by_count":113,"area":"wm"},{"id":"2510.07077","title":"Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications","created":"2025-10-08","cited_by_count":84,"area":"mllm"},{"id":"2509.19012","title":"Pure Vision Language Action (VLA) Models: A Comprehensive Survey","created":"2025-09-23","cited_by_count":49,"area":"mllm"},{"id":"2601.05230","title":"Learning Latent Action World Models In The Wild","created":"2026-01-08","cited_by_count":47,"area":"wm"},{"id":"2505.17006","title":"CoMo: Learning Continuous Latent Motion from Internet Videos for Scalable Robot Learning","created":"2025-05-22","cited_by_count":40,"area":null},{"id":"2603.22078","title":"Do World Action Models Generalize Better than VLAs? A Robustness Study","created":"2026-03-23","cited_by_count":36,"area":"wm"},{"id":"2607.08639","title":"Native Video-Action Pretraining for Generalizable Robot Control","created":"2026-07-09","cited_by_count":36,"area":null},{"id":"2511.07732","title":"ViPRA: Video Prediction for Robot Actions","created":"2025-11-11","cited_by_count":26,"area":null},{"id":"2609.10522","title":"Show-Harness: Just a VLM Agent Can Play Robots","created":"2026-09-09","cited_by_count":20,"area":"mllm"},{"id":"2603.29844","title":"DIAL: Decoupling Intent and Action via Latent World Modeling for End-to-End VLA","created":"2026-03-31","cited_by_count":19,"area":"wm"},{"id":"2602.10556","title":"LAP: Language-Action Pre-Training Enables Zero-shot Cross-Embodiment Transfer","created":"2026-02-11","cited_by_count":18,"area":"vla"},{"id":"2512.16811","title":"GeoPredict: Leveraging Predictive Kinematics and 3D Gaussian Geometry for Precise VLA Manipulation","created":"2025-12-18","cited_by_count":17,"area":"vla"},{"id":"2511.19859","title":"Unifying Perception and Action: A Hybrid-Modality Pipeline with Implicit Visual Chain-of-Thought for Robotic Action Generation","created":"2025-11-25","cited_by_count":14,"area":"lr"},{"id":"2602.00557","title":"ConLA: Contrastive Latent Action Learning from Human Videos for Robotic Manipulation","created":"2026-01-31","cited_by_count":14,"area":"vla"},{"id":"2511.16175","title":"Mantis: A Versatile Vision-Language-Action Model with Disentangled Visual Foresight","created":"2025-11-20","cited_by_count":13,"area":"vla"},{"id":"2602.10102","title":"VideoWorld 2: Learning Transferable Knowledge from Real-world Videos","created":"2026-02-10","cited_by_count":12,"area":null},{"id":"2606.05979","title":"World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis","created":"2026-06-04","cited_by_count":12,"area":"wm"},{"id":"2603.11041","title":"DynVLA: Learning World Dynamics for Action Reasoning in Autonomous Driving","created":"2026-03-11","cited_by_count":11,"area":"vla"},{"id":"2602.10104","title":"Olaf-World: Orienting Latent Actions for Video World Modeling","created":"2026-02-10","cited_by_count":10,"area":"wm"},{"id":"2603.27670","title":"ProgressVLA: Progress-Guided Diffusion Policy for Vision-Language Robotic Manipulation","created":"2026-03-29","cited_by_count":10,"area":"vla"},{"id":"2604.19734","title":"UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling","created":"2026-04-21","cited_by_count":10,"area":"wm"},{"id":"2606.09827","title":"MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models","created":"2026-06-08","cited_by_count":10,"area":"vla"},{"id":"2603.10422","title":"World2Act: Latent Action Post-Training from World Model Dynamics","created":"2026-03-11","cited_by_count":8,"area":"wm"},{"id":"2602.02259","title":"Segment to Focus: Guiding Latent Action Models in the Presence of Distractors","created":"2026-02-02","cited_by_count":6,"area":null},{"id":"2607.24744","title":"Data Pyramid for Embodied Manipulation: A Survey","created":"2026-07-27","cited_by_count":6,"area":null},{"id":"2505.12744","title":"Incentivizing Multimodal Reasoning in Large Models for Direct Robot Manipulation","created":"2025-05-19","cited_by_count":5,"area":"lr"},{"id":"2602.08602","title":"Mimic Intent, Not Just Trajectories","created":"2026-02-09","cited_by_count":5,"area":"vla"},{"id":"2602.20231","title":"UniLACT: Depth-Aware RGB Latent Action Learning for Vision-Language-Action Models","created":"2026-02-23","cited_by_count":5,"area":"vla"},{"id":"2609.05588","title":"GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation","created":"2026-09-04","cited_by_count":5,"area":"wm"},{"id":"2512.11362","title":"An Anatomy of Vision-Language-Action Models: From Modules to Milestones and Challenges","created":"2025-12-12","cited_by_count":4,"area":"vla"},{"id":"2605.04678","title":"From Pixels to Tokens: A Systematic Study of Latent Action Supervision for Vision-Language-Action Models","created":"2026-05-06","cited_by_count":4,"area":"vla"},{"id":"2605.22283","title":"Spatial Memory for Out-of-Vision Manipulation in Vision-Language-Action","created":"2026-05-21","cited_by_count":4,"area":"vla"},{"id":"2608.01397","title":"SG-WAM: Self-Guided World Modeling in Geometry-Aware Policy Space","created":"2026-08-02","cited_by_count":4,"area":"wm"},{"id":"2608.19613","title":"What Matters for Latent Actions in Robot Learning","created":"2026-08-20","cited_by_count":4,"area":null},{"id":"2601.22467","title":"CARE: Multi-Task Pretraining for Latent Continuous Action Representation in Robot Control","created":"2026-01-30","cited_by_count":3,"area":"vla"},{"id":"2602.05049","title":"VISTA: Enhancing Visual Conditioning via Track-Following Preference Optimization in Vision-Language-Action Models","created":"2026-02-04","cited_by_count":3,"area":"vla"},{"id":"2606.00054","title":"From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data","created":"2026-05-18","cited_by_count":3,"area":"vla"},{"id":"2606.21493","title":"Semi-Supervised Vision-Language-Action Model","created":"2026-06-19","cited_by_count":3,"area":"vla"},{"id":"2607.09185","title":"Causally Debiased Latent Action Model for Embodied Action Conditioned World Models","created":"2026-07-10","cited_by_count":3,"area":"wm"},{"id":"2607.26657","title":"Enfold: Folding World Model Imagination into Predictive Representations for Ultra-Efficient Embodied Control","created":"2026-07-29","cited_by_count":3,"area":"wm"},{"id":"2608.05674","title":"JoyAI-RA 0.5: Scaling Robot Manipulation Learning via Dual Action Alignment","created":"2026-08-06","cited_by_count":3,"area":"vla"},{"id":"2509.26251","title":"Seeing Space and Motion: Enhancing Latent Actions with Geometric and Dynamic Awareness for Vision-Language-Action Models","created":"2025-09-30","cited_by_count":2,"area":"vla"},{"id":"2605.12167","title":"From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation","created":"2026-05-12","cited_by_count":2,"area":null},{"id":"2606.23420","title":"Flowing With Purpose: Latent Action Guided Flow Matching Policies For Robotic Manipulation","created":"2026-06-22","cited_by_count":2,"area":"diffusion"},{"id":"2607.28624","title":"PhiZero: A World Model Built Around Physical Language","created":"2026-07-30","cited_by_count":2,"area":"wm"},{"id":"2608.04396","title":"CofactVLA: Deconfounding Vision-Language-Action Models via Counterfactual Intervention","created":"2026-08-05","cited_by_count":2,"area":"vla"},{"id":"2511.21428","title":"From Observation to Action: Latent Action-based Primitive Segmentation for VLA Pre-training in Industrial Settings","created":"2025-11-26","cited_by_count":1,"area":"vla"},{"id":"2604.10677","title":"LIDEA: Human-to-Robot Imitation Learning via Implicit Feature Distillation and Explicit Geometry Alignment","created":"2026-04-12","cited_by_count":1,"area":"offlinerl"},{"id":"2604.15215","title":"A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics","created":"2026-04-16","cited_by_count":1,"area":"offlinerl"},{"id":"2605.13452","title":"CUBic: Coordinated Unified Bimanual Perception and Control Framework","created":"2026-05-13","cited_by_count":1,"area":null},{"id":"2606.19408","title":"FlexLAM: Resolving the Bottleneck Trade-off in Latent Action Learning","created":"2026-06-17","cited_by_count":1,"area":null},{"id":"2606.28133","title":"Translation as a Bridging Action: Transferring Manipulation Skills from Humans to Robots","created":"2026-06-26","cited_by_count":1,"area":null},{"id":"2607.28394","title":"Hand-Object Interaction in the Age of Large Foundation Models:Reconstruction, Generation, and Embodied Transfer","created":"2026-07-30","cited_by_count":1,"area":null},{"id":"2608.06374","title":"DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation","created":"2026-08-06","cited_by_count":1,"area":"vla"},{"id":"2604.15196","title":"Unsupervised Skeleton-Based Action Segmentation via Hierarchical Spatiotemporal Vector Quantization","created":"2026-04-16","cited_by_count":0,"area":null},{"id":"2605.06298","title":"Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement","created":"2026-05-07","cited_by_count":0,"area":"wm"},{"id":"2606.17256","title":"Contrastive Action-Image Pre-training for Visuomotor Control","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2606.29308","title":"MirrorPPR: Exemplar-Based Portrait Photo Retouching","created":"2026-06-28","cited_by_count":0,"area":null},{"id":"2608.06994","title":"Decoupling Intention from Trajectory: A Representational Deduction Framework for World Action Models","created":"2026-08-07","cited_by_count":0,"area":"wm"},{"id":"2608.08596","title":"Goal-oriented Navigation Instruction Generation with Tour Video Priors","created":"2026-08-09","cited_by_count":0,"area":null},{"id":"2609.36518","title":"LIBERO-MAX: Do Robot Policies Adapt When the World Changes?","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.01741","title":"ATI-VLA: Action-Centric Predictive Vision-Language-Action Models via Actionable Alignment Then Adaptive Injection","created":"2026-10-01","cited_by_count":0,"area":"vla"},{"id":"2610.01742","title":"World Motion Models: Flexible Sequence Modeling of SE(3) Trajectories","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.09509","title":"TERRA: Learning Transportable Latent Actions through Temporal Effect Representation and Relational Alignment","created":"2026-10-07","cited_by_count":0,"area":null},{"id":"2610.16969","title":"UniLAP: A Unified Latent Action Policy for Cross-Embodiment Manipulation","created":"2026-10-12","cited_by_count":0,"area":null},{"id":"2610.20160","title":"OneLA: Learning Many Worlds from Latent Actions","created":"2026-10-14","cited_by_count":0,"area":"wm"},{"id":"2610.20604","title":"VITAL: From Visual Motion to Precise Action","created":"2026-10-14","cited_by_count":0,"area":"wm"}],"next_cursor":null}