优化视觉语言动作模型推理，让低成本农业机械臂更快更稳地操作农产品。
Threading Optimization for Vision-Language-Action Model Inference in Low-Cost Smart Agricultural Manipulation
- 通过线程优化改进策略推理与控制流程，降低延迟。
- 在大蒜和核桃操作任务中，响应速度和稳定性显著提升。
- 适合需要快速精准控制的低成本农业机器人场景。
视觉语言动作（VLA）模型仍面临推理速度慢、难以实现精细运动调整的问题，限制了其在工业领域的广泛应用。尽管已提出实时动作分块（RTAC）算法以解决这些瓶颈，但将算法从伪代码层面转化为低成本机械臂上的稳定实际部署仍具挑战。本文针对低成本农业操作机械臂，完整实现了面向RTAC的系统级优化。我们超越原始高层伪代码，对策略推理与控制流水线的线程实现进行优化，不改变底层策略的前提下显著降低端到端延迟，提升响应能力。实验在处理大蒜和核桃等农产品的任务中验证，结果表明，自定义线程实现相比基线版RTAC，在控制稳定性和速度上均有显著提升。
原文摘要 · Abstract (English)
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.
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