English(EN) Deep Active Inference with Diffusion Policy and Multiple Timescale World Model for Real-World Exploration and Navigation

深度主动推理框架增强机器人导航与探索能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种新颖的深度主动推理框架，用于自主机器人导航。该框架集成了用于动作生成的扩散策略，以及用于预测长远后果的多时间尺度循环状态空间模型（MTRSSM）。在真实世界场景中的实验表明，该方法能够提高成功率并减少碰撞，尤其是在需要广泛探索的任务中。 AI

影响通过主动推理统一探索与目标导向运动，增强了机器人导航能力。

排序理由该集群包含一篇详细介绍机器人导航新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Riko Yokozawa, Kentaro Fujii, Yuta Nomura, Shingo Murata · 2026-09-10 04:00

基于扩散策略和多时间尺度世界模型的深度激活推理，用于现实世界探索与导航

arXiv:2510.23258v2 Announce Type: replace-cross Abstract: Autonomous robotic navigation in real-world environments requires exploration to acquire environmental information as well as goal-directed navigation in order to reach specified targets. Active inference (AIF) based on th…