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New DIRECT framework optimizes compute for embodied AI planners

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework called DIRECT to optimize the allocation of computational resources for embodied AI planners. The system analyzes multimodal scene context to intelligently route compute, improving efficiency and reducing latency compared to fixed model selection strategies. Experiments on benchmarks and a physical robot arm demonstrated that DIRECT can achieve comparable or better success rates with significantly lower costs. AI

IMPACT Optimizes resource allocation for embodied AI, potentially enabling more efficient and cost-effective deployment of robotic systems.

RANK_REASON The cluster contains an academic paper detailing a new framework and experimental results.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New DIRECT framework optimizes compute for embodied AI planners

COVERAGE [2]

  1. 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 ·

    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,…

  2. arXiv cs.AI TIER_1 English(EN) · Marco Pavone ·

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

    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…

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