用B样条控制点实现机器人操作的实时平滑运动
ABPolicy: Asynchronous B-Spline Flow Policy for Real-Time and Smooth Robotic Manipulation
- 在B样条控制点空间异步推理，保证动作连续
- 实测轨迹抖动降低，动态任务表现更优
- 适合需要流畅响应的实时机器人场景
机器人操作需要既能平滑又可快速响应环境变化的策略。然而，原始动作空间的同步推理会引发块内抖动、块间不连续和停顿式执行等问题，损害动作的平滑性与实时性。本文提出ABPolicy，一种在B样条控制点动作空间中运行的异步流匹配策略。首先，B样条表示确保块内动作平滑；其次，通过双向动作预测与重拟合优化，实现块间连续性；最后，利用异步推理实现实时连续更新。我们在七项任务上评估了ABPolicy，涵盖静态与动态（含移动物体）场景。实验结果表明，该方法显著降低了轨迹抖动，提升了运动平滑性与整体性能。
原文摘要 · Abstract (English)
Robotic manipulation requires policies that are smooth and responsive to evolving observations. However, synchronous inference in the raw action space introduces several challenges, including intra-chunk jitter, inter-chunk discontinuities, and stop-and-go execution. These issues undermine a policy's smoothness and its responsiveness to environmental changes. We propose ABPolicy, an asynchronous flow-matching policy that operates in a B-spline control-point action space. First, the B-spline representation ensures intra-chunk smoothness. Second, we introduce bidirectional action prediction coupled with refitting optimization to enforce inter-chunk continuity. Finally, by leveraging asynchronous inference, ABPolicy delivers real-time, continuous updates. We evaluate ABPolicy across seven tasks encompassing both static settings and dynamic settings with moving objects. Empirical results indicate that ABPolicy reduces trajectory jerk, leading to smoother motion and improved performance. Project website: https://teee000.github.io/ABPolicy/.
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