LiPo: A Lightweight Post-optimization Framework for Smoothing Action Chunks Generated by Learned Policies

TL;DR

This paper introduces a lightweight post-optimization framework that smooths chunked action sequences in learned robotic policies, significantly reducing vibrations and jitter for more stable and robust manipulation tasks.

Contribution

It proposes a novel combination of inference-aware chunk scheduling, linear blending, and jerk-minimizing trajectory optimization to enhance motion smoothness in learned policies.

Findings

Reduces vibration and motion jitter in robotic manipulation

Improves mechanical robustness and motion quality

Validated on a position-controlled robotic arm performing dynamic tasks

Abstract

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.