Topic: Robotics

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1. Agentic VLA Inference Plugin for Long-Horizon Tasks in Scientific Experiments

   Paper • Feb 11, 2026 • arXiv • Junjie Wang, Zequn Xie, Dan Yang, Jie Feng, Yue Shen, Jinjie Gu

   Vision-language-action (VLA) models often suffer from the state gap issue when inferring open and long-horizon tasks in scientific scenarios. We propose an LLM-based agentic inference mechanism tha...

2. GSR: Learning Structured Reasoning for Embodied Manipulation

   Paper • Feb 10, 2026 • arXiv • Kewei Hu, Michael Zhang, Hanwen Kang

   We introduce Grounded Scene-graph Reasoning (GSR), a structured reasoning paradigm that explicitly models world-state evolution as transitions over semantically grounded scene graphs. By reasoning ...

3. Towards Physically Executable 3D Gaussian for Embodied Navigation

   Paper • Feb 12, 2026 • arXiv • Wancai Zheng, Hao Chen, Xinyi Yu

   3D Gaussian Splatting (3DGS) is a photorealistic rendering method but lacks semantics and physical executability for Visual-Language Navigation (VLN). We propose SAGE-3D, a paradigm that upgrades 3...

4. ZEST: Zero-shot Embodied Skill Transfer for Athletic Robot Control

   Paper • Feb 8, 2026 • arXiv • Eva Mungai, Zach Nobles, Scott Kuindersma, Yeuhi Abe

   We introduce ZEST (Zero-shot Embodied Skill Transfer), a motion-imitation framework that trains policies via RL from diverse sources—mocap, noisy monocular video, and animation—and deploys them to ...

5. Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks

   Paper • Feb 13, 2026 • arXiv • Zhihong Liu, Yang Li, Rengming Huang, Cewu Lu

   Open world language conditioned task planning is crucial for robots in large-scale households. A key challenge remains scalability; performance degrades with increasing environment size and plan le...

6. VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model

   Paper • Feb 12, 2026 • arXiv • Xiang Liu, Guocai Yao, Min Zhang, Changshui Zhang

   The goal of this paper is to improve VLA performance through iterative online interaction. Since collecting real-world rollouts is expensive, we investigate whether a learned simulator—an action-co...

7. Scaling Verification Can Be More Effective than Scaling Policy Learning for VLA

   Paper • Feb 13, 2026 • arXiv • Jacky Kwok, Xilun Zhang, Marco Pavone, Chelsea Finn

   We investigate test-time verification as a means to shrink the 'intention-action gap' in embodied AI. We characterize the test-time scaling law for embodied instruction following and demonstrate th...