ABPolicy：异步B样条流策略用于实时和流畅的机器人操控
ABPolicy: Asynchronous B-Spline Flow Policy for Real-Time and Smooth Robotic Manipulation
AI总结：
ABPolicy通过异步B样条流匹配策略实现实时、平滑的机器人操控，减少轨迹冲击并提升性能。
摘要：
机器人操控需要能够平滑且响应不断变化观察的策略。然而，在原始动作空间中进行同步推断会带来几个挑战，包括片段内抖动、片段间不连续性和停停走走的执行。这些问题会削弱策略的平滑性和对环境变化的响应能力。我们提出了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样条表示确保片段内平滑。其次，我们引入双向动作预测并结合再拟合优化以强制片段间连续性。最后，通过利用异步推断，ABPolicy实现了实时、连续的更新。我们评估了ABPolicy在七个任务上的表现，涵盖静态和动态设置，其中包含移动物体。实证结果表明，ABPolicy减少了轨迹冲击，导致更平滑的运动和改进的性能。项目网站：https://teee000.github.io/ABPolicy/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
