cs.RO · 2026-07-07 · No. 46

Robotics, 2026-07-07.

7 new papers in cs.RO. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

7 entries
  1. 01

    Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation

    Jiaqi Peng, Xiqian Yu, Delin Feng, Yuqiang Yang, Wenzhe Cai, Jing Xiong, Ganlin Yang, Jinliang Zheng, Jiafei Cao,...

    cs.RO · cs.AI · cs.CV

    While recent Vision-Language-Action (VLA) models show promise toward generalist manipulation policies, they struggle with long-horizon tasks due to their Markovian nature-relying solely on current observations. Hierarchical dual-system methods address this but suffer from a gap between high-level planning semantics and low-level execution kinematics. We introduce Cortex, a bidirectionally aligned embodied agent framework with a customized...

    arxiv.org/abs/2607.05377 · PDF

  2. 02

    GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks

    Kaiyuan Chen, Shuangyu Xie, Letian Fu, Justin Yu, William Pacini, Sandeep Bajamahal, Hudson Kim, Jaimyn Drake,...

    cs.RO · cs.AI · cs.CL · cs.LG

    For robots to work reliably in commercial and industrial applications, can recent advances in agentic coding systems combine interpretable robot programming with the open-world adaptability of model-free policies? We focus on "Variational Automation" (VA), a class of tasks that have larger variations in object geometry and pose than fixed automation. Model-free policies often struggle to close the reliability gap for VA tasks, which must be...

    arxiv.org/abs/2607.05369 · PDF

  3. 03

    Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales

    Yang Li, Feng Xue, Fan Mo, Yunhao Liu, Jianhong Wang, Ying Wen, Qingrui Zhang, Shaoshuai Mou, Wei Pan

    cs.RO · cs.AI

    Deploying robot teams in the real world requires simultaneous adaptation to unseen environments, unknown partners, and varying team sizes, yet existing approaches often address these challenges in isolation under the closed-world assumption of fixed teammates. We formalize this as open adaptive multi-robot teaming and propose a hypergraphic-form game formulation that captures team-level cooperative relationships beyond pairwise interactions,...

    arxiv.org/abs/2607.04972 · PDF

  4. 04

    DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation

    Jian Zhu, Jianjun Zhang, Taiyi Su, Tianbin Liu, Zhangyuan Wang, Kai Xie, Zitai Huang, Chong Ma, Youzhang He,...

    cs.RO · cs.AI

    World Action Models (WAMs) provide a promising alternative to Vision-Language-Action (VLA) policies by using video-based world modeling as dense supervision for robot action learning. Existing WAMs excel at physically grounded execution, but typically lack the explicit language-level planning interface in VLM-based VLAs for decomposing coarse instructions. Such decomposition becomes important when household tasks involve complex multi-step...

    arxiv.org/abs/2607.04927 · PDF

  5. 05

    Geometry-Aware Motion Latents for Learning Robust Manipulation Policies

    Yunchao Zhang, Yijia Weng, Ruizhe Liu, Ming Hu, Leonidas Guibas, Yanchao Yang

    cs.RO · cs.AI

    Learning motion latents for robotic manipulation heavily relies on extracting motion patterns from visual sequences, yet effective action abstractions require understanding three-dimensional geometric transformations. Here, we introduce GeoMoLa (Geometry-Aware Motion Latents), which learns discrete motion latent codes by predicting how point clouds evolve during manipulation rather than reconstructing visual observations. This...

    arxiv.org/abs/2607.04714 · PDF

  6. 06

    Do Vision-Language-Action Models Mean What They Say? On the Role of Faithfulness in Embodied Reasoning

    Matthew Foutter, Matteo Cercola, Lena Wild, Yunshan Wang, Michelle Li, Daniele Gammelli, Marco Pavone

    cs.RO · cs.AI

    Embodied Chain-of-Thought has emerged as a promising mechanism to enhance robot decision-making and interpretability in black-box Vision-Language Action (VLA) models. However, whether this verbalized Chain-of-Thought truthfully reflects the policy's underlying decision process remains poorly understood. We distinguish between functional reasoning, in which reasoning improves task performance, and faithful reasoning, in which reasoning truly...

    arxiv.org/abs/2607.04681 · PDF

  7. 07

    SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing

    Stone Tao, Jie Xu, Hesam Rabeti, Yashraj Narang, Yijie Guo, Iretiayo Akinola

    cs.RO · cs.AI

    Linear-deformable manipulation remains challenging due to the complex deformations of objects such as cables and ropes. Prior data-driven approaches, particularly imitation learning, have shown some promise in narrowly defined settings but typically require thousands of demonstrations for specific tasks and cable types, limiting scalability and generalization. We introduce a sim-to-real reinforcement learning (RL) framework for multi-stage...

    arxiv.org/abs/2607.04616 · PDF

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