English(EN) DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?

新的DIRECT框架优化具身AI规划器的计算

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-10 17:58

研究人员开发了一个名为DIRECT的新框架，用于优化具身AI规划器的计算资源分配。该系统分析多模态场景上下文以智能地路由计算，与固定的模型选择策略相比，提高了效率并降低了延迟。在基准测试和物理机器人手臂上的实验表明，DIRECT可以以显著更低的成本实现相当或更好的成功率。 AI

影响优化具身AI的资源分配，可能实现更高效、更具成本效益的机器人系统部署。

排序理由该集群包含一篇详细介绍新框架和实验结果的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Jadelynn Dao, Milan Ganai, Yasmina Abukhadra, Ajay Sridhar, Mozhgan Nasr Azadani, Katie Luo, Clark Barrett, Jiajun Wu, Chelsea Finn, Marco Pavone · 2026-06-11 04:00

DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?

arXiv:2606.12402v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as high-level planners for embodied agents, with an emerging strategy of scaling test-time compute to improve capability. However, we observe that doing so increases latency,…
arXiv cs.AI TIER_1 English(EN) · Marco Pavone · 2026-06-10 17:58

直击：何时何地应在具身规划器中分配测试时间计算？

Vision-Language Models (VLMs) are increasingly deployed as high-level planners for embodied agents, with an emerging strategy of scaling test-time compute to improve capability. However, we observe that doing so increases latency, token usage, and FLOPs while yielding uneven, oft…