cs.RO · 2026-07-26 · No. 65

Robotics, 2026-07-26.

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

01 — The papers

5 entries
  1. 01

    GS-Agent: Creating 4D Physical Worlds With Generative Simulation

    Hongxin Zhang, Chunru Lin, Junyan Li, Zhou Xian, Tsun-Hsuan Wang, Chuang Gan

    cs.RO · cs.AI · cs.CL · cs.CV

    Creating dynamic and physically realistic 4D worlds from natural language descriptions is both fascinating and challenging. Traditional computer graphics methods rely on manual creation, requiring extensive human effort to fine-tune materials, motions, and visual fidelity. Recent advances in generative foundation models have sparked interest in learning to generate such 4D worlds from large-scale data; however, existing methods still struggle...

    arxiv.org/abs/2607.21522 · PDF

  2. 02

    VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method

    Jiabin Lou, Haopeng Wang, Yuanshuai Wang, Xinyu Liu, Xuxin Lv, Yuxin Guo, Lei Huang, Rongye Shi, Wenjun Wu

    cs.RO · cs.AI

    Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions. However, route-level instructions commonly encode spatial priors, such as orientation, distance, and layout, that are not explicitly available from onboard sensing at deployment in open, GPS-denied environments. Benchmark performance under such interfaces therefore jointly reflects visual navigation ability and the use of route structure...

    arxiv.org/abs/2607.21400 · PDF

  3. 03

    Robostral Navigate

    Arjun Majumdar, Avinash Sooriyarachchi, Benjamin Tibi, Chris Bamford, Elliot Chane-Sane, Guillaume Lample, Khyathi...

    cs.RO · cs.AI

    Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently. Yet, today's best systems depend on depth sensors, multi-camera rigs, or pre-built maps, limiting the hardware they support and increasing deployment cost. We introduce Robostral Navigate, an 8B vision-language model built around this scalability objective. The model consumes only a stream of...

    arxiv.org/abs/2607.20785 · PDF

  4. 04

    Emergent Compositional Skills in Mixture-of-Experts VLAs

    Shlok Shah, Rhiaan Jhaveri, Tharun Kumar Tiruppali Kalidoss, Chirayu Nimonkar, Ishaan Javali

    cs.RO · cs.AI · cs.LG

    We consider the problem of learning compositional robot policies end-to-end from expert demonstrations, without any pre-specified notion of task decomposition or hierarchy. We ask whether a VLA trained with a simplified Mixture-of-Experts (MoE) action head can emergently learn to decompose tasks into reusable, interpretable primitives. We find that learned experts are heavily reused across tasks and consistently correspond to qualitatively...

    arxiv.org/abs/2607.20771 · PDF

  5. 05

    Self-Supervised Bio-Inspired Robotic Trajectory Planning with Obstacle Avoidance

    Miroslav Krupa, Miroslav Cibula, Kristína Malinovská

    cs.RO · cs.AI · cs.LG

    Trajectory planning is a fundamental problem in robotics, requiring the generation of collision-free and efficient trajectories in a potentially complex environment. While sampling-based planners remain the dominant approach, they are often computationally expensive, particularly in high-dimensional spaces and obstacle-rich environments. Methods based on model learning offer a promising alternative, enabling efficient planning through a...

    arxiv.org/abs/2607.20743 · PDF

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