让机器人动作更顺滑，解决学习策略的断点问题。
LiPo: A Lightweight Post-optimization Framework for Smoothing Action Chunks Generated by Learned Policies
- 通过重叠分块与线性融合，减少动作切换时的突变。
- 在有限扰动范围内优化轨迹，显著降低振动和抖动。
- 适合需要稳定动态操作的机器人应用，如抛掷或举重。
模仿学习的进展使机器人能在非结构化环境中执行越来越复杂的操作任务。然而，大多数学习策略依赖离散的动作分块，导致分块边界处出现不连续，降低运动质量，尤其在投掷或搬运重物等动态任务中，平滑轨迹对动量传递和系统稳定性至关重要。本文提出一种轻量级后优化框架，用于平滑分块动作序列。方法包含三个核心组件：(1) 推理感知的分块调度，主动生成重叠分块以避免推理延迟带来的停顿；(2) 在重叠区域采用线性融合，减少突变过渡；(3) 在有界扰动空间内进行急动度最小化的轨迹优化。在位置控制机械臂上验证了该方法在动态操作任务中的有效性。实验结果表明，该方法显著降低了振动与运动抖动，实现更平稳的执行并提升机械鲁棒性。
原文摘要 · Abstract (English)
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.
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