通过增量迭代参考学习控制细化加速演示
Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning
AI总结：
本文提出I2RLC方法，通过逐步提升速度并更新参考轨迹，实现高保真度的加速演示，提升接触密集模仿学习的效率与稳定性。
摘要：
快速执行接触密集的操控任务对实际应用至关重要，但提供快速演示进行模仿学习仍具挑战：人类无法高速演示，直接加速演示会改变接触动态并导致大跟踪误差。本文提出一种方法，通过重新利用迭代参考学习控制（IRLC）来迭代更新参考轨迹，以自主细化加速演示。然而，直接在高速下应用IRLC会生成更大的早期迭代误差和不稳定的暂态。为此，我们提出增量迭代参考学习控制（I2RLC），逐步提高速度并更新参考轨迹，从而产生高保真轨迹。我们在真实机器人白板擦除和圆柱体插入孔任务中使用遥控装置进行验证，使用具有柔顺控制跟随器和3D打印的力反馈领导者。IRLC和I2RLC均实现高达10倍的加速演示，同时减少跟踪误差；此外，I2RLC在三个任务和多个速度（3x-10x）下平均比IRLC提高22.5%的空间相似性。然后使用细化轨迹训练IL策略；所得到的策略在演示后执行更快，且在圆柱体插入孔任务中在可见和不可见位置均实现100%的成功率，I2RLC训练的策略表现出比IRLC训练的策略更低的接触力。这些结果表明，渐进速度调度与参考适应为快速、接触密集的IL提供了实用路径。
英文摘要：
Fast execution of contact-rich manipulation is critical for practical deployment, yet providing fast demonstrations for imitation learning (IL) remains challenging: humans cannot demonstrate at high speed, and naively accelerating demonstrations alters contact dynamics and induces large tracking errors. We present a method to autonomously refine time-accelerated demonstrations by repurposing Iterative Reference Learning Control (IRLC) to iteratively update the reference trajectory from observed tracking errors. However, applying IRLC directly at high speed tends to produce larger early-iteration errors and less stable transients. To address this issue, we propose Incremental Iterative Reference Learning Control (I2RLC), which gradually increases the speed while updating the reference, yielding high-fidelity trajectories. We validate on real-robot whiteboard erasing and peg-in-hole tasks using a teleoperation setup with a compliance-controlled follower and a 3D-printed haptic leader. Both IRLC and I2RLC achieve up to 10x faster demonstrations with reduced tracking error; moreover, I2RLC improves spatial similarity to the original trajectories by 22.5% on average over IRLC across three tasks and multiple speeds (3x-10x). We then use the refined trajectories to train IL policies; the resulting policies execute faster than the demonstrations and achieve 100% success rates in the peg-in-hole task at both seen and unseen positions, with I2RLC-trained policies exhibiting lower contact forces than those trained on IRLC-refined demonstrations. These results indicate that gradual speed scheduling coupled with reference adaptation provides a practical path to fast, contact-rich IL.
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。
