DIRECT Framework Optimizes Test-Time Compute Allocation for Vision-Language Model Planners in Robotics
Researchers have introduced DIRECT, a routing framework that dynamically allocates computational resources for Vision-Language Models (VLMs) used as planners in embodied robotic agents. Current approaches to scaling test-time compute in these systems increase latency, token usage, and processing costs while producing uneven performance gains. DIRECT addresses this inefficiency by using multimodal scene context to decide when and where to spend compute, achieving frontier-level planning performance at significantly lower cost.
A team of researchers has proposed DIRECT, a compute-routing framework designed to optimize how Vision-Language Models (VLMs) allocate test-time computation when acting as high-level planners for embodied agents such as robots. The core observation motivating the work is that naively scaling test-time compute—through deeper chain-of-thought reasoning, larger models, or longer memory histories—increases latency, token usage, and FLOPs without producing uniform improvements in task success rates. DIRECT instead uses multimodal scene context to route each prompt to an appropriate level of compute, improving the success-to-cost trade-off compared to fixed model selection strategies. Experiments conducted on the VLABench and RoboMME benchmarks revealed that the three primary scaling axes yield qualitatively different capability gains, meaning no single axis is universally optimal. The framework was also validated on a physical Franka robotic arm in a DROID setup covering zero-shot manipulation and long-horizon task chaining, where DIRECT matched or exceeded the performance of stronger, more expensive models at up to 65% lower average latency. The authors argue that intelligent compute allocation, rather than blanket scaling, is the key to deploying frontier AI planning capabilities in real-world robotic systems.
What's missing
The study does not report results across diverse robot hardware platforms beyond the Franka arm, leaving generalizability to other embodied systems an open question. It is also unclear how DIRECT's routing decisions perform under distribution shift—i.e., when scene contexts at deployment differ substantially from those seen during router training.
What different sources said
- arXiv cs.AICenter
DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
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