AI planners use new DIRECT framework to optimize compute allocation

By PulseAugur Editorial · [1 sources] · 2026-06-10 17:58

Researchers have developed a new framework called DIRECT to optimize the allocation of test-time compute for embodied AI planners. This method uses multimodal scene context to dynamically route compute, improving performance-cost trade-offs compared to fixed model selection. Experiments on simulated and physical robotic tasks demonstrate that DIRECT can achieve comparable success rates to stronger models at significantly lower latency and cost. AI

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

RANK_REASON The cluster contains a research paper detailing a new method for embodied AI planners. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

arXiv cs.AI TIER_1 English(EN) · Marco Pavone · 2026-06-10 17:58

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…

COVERAGE [1]

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

RELATED ENTITIES

RELATED TOPICS