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机器人

51 篇 · 资讯流
  2. arXiv cs.RO（机器人）

    MedVLA: A Hierarchical Vision-Language-Action Framework for Closed-Loop Precision Medical Robot Manipulation

    Precision medical robotics demands adaptive decision-making under strict safety, interpretability, and execution constra

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  4. arXiv cs.RO（机器人）

    Multi-Agent Transportation of Free-Flyers in Microgravity Via Pushing Interaction Under Human-in-the-Loop Control

    We propose a safety-critical framework for the cooperative transportation of passive targets in microgravity, where a te

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  6. arXiv cs.RO（机器人）

    "Dear LLaVA, Please Drive": A Depth-Aware Vision-Language Agent for Closed-Loop Robotic Control

    Vision-language models (VLMs) provide a compelling foundation for reasoning-driven mobile navigation, offering rich cont

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  7. arXiv cs.RO（机器人）

    Riemannian Density-Driven Optimal Control: Tangent-Space LQR for Second-Order Multi-Agent Systems on Curved Manifolds

    Density-Driven Optimal Control (D2OC) provides an effective framework for steering multi-agent systems toward prescribed

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  9. arXiv cs.RO（机器人）

    MAAP: Multi-Agent Active Perception for Collaborative Manipulation

    Multi-agent manipulation naturally produces multiple task-driven viewpoints: every arm carries a wrist camera and moves

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  10. arXiv cs.RO（机器人）

    AtomEgo: Exploring Ego-Robot Integration for Embodied Foundation Model Pretraining

    Embodied foundation models are constrained by the limited scale and diversity of robot demonstrations, motivating the us

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  11. arXiv cs.RO（机器人）

    StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation

    Hierarchical planning frameworks combine skills from multiple robot control policies for long-horizon task execution, wh

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  12. arXiv cs.RO（机器人）

    MoWAM: Explicit Future Motion Prediction for Efficient World Action Models

    World Action Models (WAMs) improve robot policy learning by incorporating future dynamics, yet explicitly generating fut

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  14. arXiv cs.RO（机器人）

    Semantic SLAM in Precision Agriculture using Bayesian Inference

    This paper presents a real-time semantic world modeling framework specialized for precision agriculture using autonomous

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  15. arXiv cs.RO（机器人）

    Worst-Case Hidden-Vehicle Trajectory Search in Spatiotemporal Occlusion Regions

    Occlusion creates fundamental uncertainty in autonomous driving. Existing methods often propagate frame-wise hypotheses

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