LiPo：一种用于平滑学习策略生成的动作分块的轻量级后优化框架
LiPo: A Lightweight Post-optimization Framework for Smoothing Action Chunks Generated by Learned Policies
AI总结：
针对学习策略动作分块边界不连续导致的运动抖动问题，本文提出轻量级后优化框架LiPo，通过推理感知重叠分块、线性混合与加加速度最小化轨迹优化实现平滑执行。
摘要：
模仿学习的最新进展使机器人能够在非结构化环境中执行日益复杂的操作任务。然而，大多数学习策略依赖离散的动作分块，这会在分块边界处引入不连续性。这些不连续性会降低运动质量，在投掷或举起重物等动态任务中尤为严重，因为平滑轨迹对动量传递和系统稳定性至关重要。本文提出了一种轻量级后优化框架，用于平滑分块动作序列。该方法结合了三个关键组成部分：（1）推理感知的分块调度，主动生成重叠分块并避免因推理延迟导致的停顿；（2）在重叠区域进行线性混合以减少突变；（3）在受限扰动空间内进行最小化加加速度的轨迹优化。所提方法在一台执行动态操作任务的位置控制机械臂上进行了验证。实验结果表明，该方法显著减少了振动和运动抖动，从而实现更平滑的执行并提高机械鲁棒性。
英文摘要：
Recent advances in imitation learning have enabled robots to perform increasingly complex manipulation tasks in unstructured environments. However, most learned policies rely on discrete action chunking, which introduces discontinuities at chunk boundaries. These discontinuities degrade motion quality and are particularly problematic in dynamic tasks such as throwing or lifting heavy objects, where smooth trajectories are critical for momentum transfer and system stability. In this work, we present a lightweight post-optimization framework for smoothing chunked action sequences. Our method combines three key components: (1) inference-aware chunk scheduling to proactively generate overlapping chunks and avoid pauses from inference delays; (2) linear blending in the overlap region to reduce abrupt transitions; and (3) jerk-minimizing trajectory optimization constrained within a bounded perturbation space. The proposed method was validated on a position-controlled robotic arm performing dynamic manipulation tasks. Experimental results demonstrate that our approach significantly reduces vibration and motion jitter, leading to smoother execution and improved mechanical robustness.
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。
