Threading Optimization for Vision-Language-Action Model Inference in Low-Cost Smart Agricultural Manipulation
This work addresses the practical deployment of VLA models on low-cost hardware for agricultural robotics, but the contribution is incremental as it focuses on engineering optimization rather than algorithmic novelty.
The authors implemented a threading-optimized version of the RTAC algorithm for low-cost robotic arms, achieving improved control stability and speed in agricultural manipulation tasks (garlic bulbs and walnuts) compared to the base implementation.
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.