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

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

Keith Truongcao, Christopher Nhu, Zijian An, Phong Nguyen, Siwei Cai and Lifeng Zhou
IEEE International Conference on Automation Science and Engineering (CASE), pp 540-545
17 Aug 2026
DOI: https://doi.org/10.1109/CASE69030.2026.11704444

Abstract

Arm Hardware Modeling Printing Real-time systems Robots Timing Architecture Computer Architecture
Vision-Language Action (VLA) models face challenges like slow inference speed and poor fine-grained motion control, limiting their industrial adoption. 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.

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