{"total":17,"rows":[{"id":"2606.17846","title":"Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models","created":"2026-06-16","cited_by_count":50,"area":null},{"id":"2605.15153","title":"Pelican-Unify 1.0: A Unified Embodied Intelligence Model for Understanding, Reasoning, Imagination and Action","created":"2026-05-14","cited_by_count":8,"area":null},{"id":"2606.30534","title":"Orca: The World is in Your Mind","created":"2026-06-29","cited_by_count":6,"area":null},{"id":"2607.25895","title":"HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone","created":"2026-07-28","cited_by_count":4,"area":null},{"id":"2608.19613","title":"What Matters for Latent Actions in Robot Learning","created":"2026-08-20","cited_by_count":4,"area":null},{"id":"2605.24869","title":"Lngram: N-gram Conditional Memory in Latent Space","created":"2026-05-24","cited_by_count":3,"area":null},{"id":"2604.21241","title":"CorridorVLA: Explicit Spatial Constraints for Generative Action Heads via Sparse Anchors","created":"2026-04-23","cited_by_count":1,"area":null},{"id":"2609.03889","title":"FWBC-VLA: Force-Aware Whole-Body Compensation for Contact-Rich Loco-Manipulation","created":"2026-09-03","cited_by_count":1,"area":null},{"id":"2609.25627","title":"MachEmbodied-U0: Unified Understanding and Generation Model for Embodied Intelligence","created":"2026-09-22","cited_by_count":1,"area":null},{"id":"2605.28132","title":"Which Pretraining Paradigm Better Serves Spatial Intelligence? An Empirical Comparison of Vision-Language and Video Generation Models","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2607.02646","title":"EVA-Client: A Unified Data Collection, Inference, and Deployment Framework for Embodied Policies on Real Robots","created":"2026-07-02","cited_by_count":0,"area":null},{"id":"2608.16556","title":"DeepInsight II: One Trace from Benchmark to Robot","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2609.24124","title":"ActiveArena: Benchmarking and Understanding Active Perception in Robotic Manipulation","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2609.39601","title":"GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2609.40341","title":"Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?","created":"2026-09-30","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":"2610.18352","title":"Let the Mind Conceive, Let the Hand Achieve: Following Multimodal Instructions for Long-Horizon Robotic Manipulation","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}