Threading Optimization for Vision-Language-Action Model Inference in Low-Cost Smart Agricultural Manipulation

2026-05-30

AI Summary

Existing Vision-Language-Action models suffer from high inference latency and inadequate fine-grained motion control on low-cost agricultural robots, hindering real-world deployment. This work addresses these limitations by introducing a customized multithreaded scheduling scheme for the Real-Time Action Chunking (RTAC) algorithm, which restructures the inference and control pipeline without altering the underlying policy. The proposed approach significantly reduces end-to-end latency and is efficiently adapted to resource-constrained embedded robotic arms. Evaluated on garlic and walnut grasping tasks, the method demonstrates substantial improvements over the original RTAC implementation in terms of system responsiveness, control stability, and real-time performance.

Abstract

Vision-Language Action (VLA) models continue to face challenges such as slow inference speed and difficulty performing fine-grained motion adjustments, limiting their widespread adoption in industry. While the Real-Time Action Chunking (RTAC) algorithm has been proposed to address these bottlenecks, bridging the gap between the algorithm provided in pseudocode to a stable, real-world deployment on a low-cost robotic arm remains a challenge. In this work, we present a complete system-level implementation of RTAC tailored for a low-cost robotic manipulation system. We advance beyond the original high-level pseudocode by optimizing the threading implementation for the policy inference and control pipeline, reducing end-to-end latency and improving responsiveness without modifying the underlying policy. We evaluate this system on tasks involving the manipulation of agricultural produce, specifically garlic bulbs and walnuts. Experimental results demonstrate that our custom threading implementation significantly improves control stability and speed compared to the base implementation of RTAC.

Problem

• Vision-Language-Action Model
• Inference Latency
• Real-Time Action Chunking
• Low-Cost Robotic Manipulation
• Agricultural Automation

Innovation

• threading optimization
• vision-language-action model
• real-time action chunking
• low-cost robotic manipulation
• inference latency