机器人
66 篇 · 资讯流MATE: Multi-Agent Virtual Teleoperation Platform for Humanoid Collaboration Data Collection
Humanoid robots require diverse embodied experiences to acquire complex loco-manipulation and collaborative skills. Howe
原文 ↗Hierarchical Floorplan-Guided Vision-Language Exploration for Embodied Question Answering
Embodied Question Answering (EQA) requires an agent to explore a previously unseen environment, gather relevant informat
原文 ↗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
原文 ↗D-JEPA: A Decision-Aligned Latent World Model
Latent world models predict the consequences of actions, but accurate prediction does not guarantee that latent distance
原文 ↗Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies
Vision-Language-Action (VLA) models map observations to actions with no objective that accounts for how the world respon
原文 ↗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
原文 ↗具身智能模型公司方奇科技完成千万级天使轮融资
近日，具身智能通用大脑研发商方奇科技（北京）有限公司（以下简称“方奇科技”）宣布完成千万级天使轮融资，投资方包括启迪之星创投等知名机构。 据悉，本轮融资资金将主要用于核心技术研发与研发团队扩充，并加速关键技术在典型场景中的验证与商业化落地，
原文 ↗贾跃亭的法拉第未来一口气发布九款配置 EAI 机器人，最贵超 92 万元
IT之家 9 月 20 日消息，法拉第未来（Faraday Future，简称 FF）于美国当地时间 9 月 19 日举行了 919 FF EAI 机器人“四核全智”系列新品发布会，发布了 FF All-New Futurist、FF Ma
原文 ↗"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
原文 ↗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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