GATO让机器人控制在毫秒级完成上百次轨迹优化，大幅提速。
GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control
- 用多层级并行计算加速批量轨迹优化，兼顾速度与通用性。
- 实测比CPU快18-21倍，比现有GPU方案快1.4-16倍，支持实时运行。
- 适合需要快速响应的工业机械臂等机器人系统，代码开源可复现。
尽管模型预测控制（MPC）在机器人应用中表现优异，但在线求解非线性轨迹优化（TO）问题仍存在巨大计算压力。现有基于GPU的加速方法要么仅并行单次求解，要么在大规模批次下无法达到实时性能，或为提速牺牲模型通用性。这导致许多前沿MPC应用在每批数十至百余次求解时面临性能瓶颈。为此，我们提出GATO——一个开源、面向算法-软件-硬件协同设计的GPU加速批量轨迹优化求解器，专为中等批次规模实现实时吞吐而生。其通过块级、波前级和线程级的并行机制，在求解内部及跨求解间实现超高性能。我们在模拟基准测试中验证：相比CPU基线提速18–21倍，相比现有GPU基线提速1.4–16倍；案例研究显示其具有更优的抗干扰能力和收敛行为；并在工业机械臂硬件上完成了实际验证。GATO已开源，以支持可复现性和广泛应用。
原文摘要 · Abstract (English)
While Model Predictive Control (MPC) delivers strong performance across robotics applications, solving the underlying (batches of) nonlinear trajectory optimization (TO) problems online remains computationally demanding. Existing GPU-accelerated approaches either parallelize single solves, handle large batches at sub-real-time rates, or sacrifice model generality for speed. This leaves a large gap in solver performance for many state-of-the-art MPC applications that require real-time batches of tens to low-hundreds of solves. As such, we present GATO, an open source, GPU-accelerated, batched TO solver co-designed across algorithm, software, and computational hardware to deliver real-time throughput for these moderate batch size regimes. Our approach leverages a combination of block-, warp-, and thread-level parallelism within and across solves for ultra-high performance. We demonstrate the effectiveness of our approach through a combination of: simulated benchmarks showing speedups of 18-21x over CPU baselines and 1.4-16x over GPU baselines as batch size increases; case studies highlighting improved disturbance rejection and convergence behavior; and finally a validation on hardware using an industrial manipulator. We open source GATO to support reproducibility and adoption.
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