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

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.

• The paper demonstrates that LiPo significantly reduces action discontinuities using inference-aware chunk scheduling to improve trajectory smoothness.
• It integrates linear blending in overlap regions and jerk-minimizing optimization to enhance mechanical robustness during dynamic tasks.
• Experimental results on a position-controlled robotic arm reveal improved stability and high success rates in dynamic manipulation tasks.

LiPo: Enhancing Learned Policies with Lightweight Trajectory Smoothing

The presented paper introduces LiPo, a lightweight framework aimed at enhancing the trajectories of robotic actions derived from imitation learning and reinforcement learning policies. This methodology addresses a specific problem encountered with discrete action chunking in learned policies: the discontinuities at chunk boundaries that lead to degraded motion quality. These discontinuities are especially problematic in dynamic tasks, where smooth trajectories are essential for effective momentum transfer and stability.

The authors propose a post-optimization framework consisting of three key components: inference-aware chunk scheduling, linear blending, and jerk-minimizing trajectory optimization. These work together to provide smoother and more robust robotic movements. Particularly, the linear blending in the overlap region reduces abrupt transitions, while jerk-minimizing optimization ensures smooth action sequences within a constrained perturbation space.

Strong Numerical Results and Claims

Experimentally, LiPo is validated on a position-controlled robotic arm tasked with dynamic manipulation challenges. The authors report significant reductions in vibration and motion jitter, resulting in enhanced mechanical robustness. Specific numerical measures of improvement are not detailed in the summary; however, the high success rates in tasks such as the ball toss and pouch throw (e.g., 90% success rate with quintic spline) underscore the framework's efficacy. The paper also compares the method's performance with raw actions and temporal ensemble (TE), noting the instability of the robot during dynamic tasks when using TE.

Theoretical and Practical Implications

Theoretically, LiPo contributes to the enhancement of imitation and reinforcement learning methods by addressing deficiencies in action chunk implementation. By ensuring smooth transitions between discrete actions, this framework creates a pathway for more reliable task execution, thereby allowing complex manipulation tasks to be handled more effectively by robotic systems. Practically, it facilitates real-world applications where dynamic and smooth motions are critical—such as autonomous package delivery, construction, and search and rescue operations—while ensuring physical plausibility and preserving the intent of learned actions.

Speculation on Future Developments

Looking forward, the framework opens avenues for further exploration and enhancement. Future developments might involve incorporating dynamics constraints, such as torque limits, to refine the framework's applicability across different robotic systems. Additionally, extending the method to handle contact-aware blending could significantly improve performance in tasks involving variable contact dynamics. Real-time adaptation and closed-loop optimization under dynamic constraints could further enhance robustness and efficiency.

Overall, LiPo offers a practical, lightweight solution to the problem of discontinuous action sequences in learned policies, with promising improvements for robotic manipulation tasks. By refining trajectories in real-time, it broadens the scope of tasks achievable by robots and contributes to the ongoing advancement of robotic autonomy in dynamic environments.