SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies

Nadun Ranawaka Arachchige, Zhenyang Chen, Wonsuhk Jung, Woo Chul Shin, Rohan Bansal, Pierre Barroso, Yu Hang He, Yingyan Celine Lin, Benjamin Joffe, Shreyas Kousik, Danfei Xu
Proceedings of The 9th Conference on Robot Learning, PMLR 305:721-749, 2025.

Abstract

Offline Imitation Learning (IL) methods such as Behavior Cloning are effective at acquiring complex robotic manipulation skills. However, existing IL-trained policies are confined to execute the task at the same speed as shown in demonstration data. This limits the task throughput of a robotic system, a critical requirement for applications such as industrial automation. In this paper, we introduce and formalize the novel problem of enabling faster-than-demonstration execution of visuomotor policies and identify fundamental challenges in robot dynamics and state-action distribution shifts. We instantiate the key insights as SAIL (Speed Adaptation for Imitation Learning), a full-stack system integrating four tightly-connected components: (1) a consistency-preserving action inference algorithm for smooth motion at high speed, (2) high-fidelity tracking of controller-invariant motion targets, (3) adaptive speed modulation that dynamically adjusts execution speed based on motion complexity, and (4) action scheduling to handle real-world system latencies. Experiments on 12 tasks across simulation and two real, distinct robot platforms shows that SAIL achieves up to a {4$\times$ speedup} over demonstration speed in simulation and up to {3.2$\times$ speedup} in the real world. Additional detail is available at https://sail-robot.github.io

Cite this Paper

BibTeX
@InProceedings{pmlr-v305-arachchige25a, title = {SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies}, author = {Arachchige, Nadun Ranawaka and Chen, Zhenyang and Jung, Wonsuhk and Shin, Woo Chul and Bansal, Rohan and Barroso, Pierre and He, Yu Hang and Lin, Yingyan Celine and Joffe, Benjamin and Kousik, Shreyas and Xu, Danfei}, booktitle = {Proceedings of The 9th Conference on Robot Learning}, pages = {721--749}, year = {2025}, editor = {Lim, Joseph and Song, Shuran and Park, Hae-Won}, volume = {305}, series = {Proceedings of Machine Learning Research}, month = {27--30 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v305/main/assets/arachchige25a/arachchige25a.pdf}, url = {https://proceedings.mlr.press/v305/arachchige25a.html}, abstract = {Offline Imitation Learning (IL) methods such as Behavior Cloning are effective at acquiring complex robotic manipulation skills. However, existing IL-trained policies are confined to execute the task at the same speed as shown in demonstration data. This limits the task throughput of a robotic system, a critical requirement for applications such as industrial automation. In this paper, we introduce and formalize the novel problem of enabling faster-than-demonstration execution of visuomotor policies and identify fundamental challenges in robot dynamics and state-action distribution shifts. We instantiate the key insights as SAIL (Speed Adaptation for Imitation Learning), a full-stack system integrating four tightly-connected components: (1) a consistency-preserving action inference algorithm for smooth motion at high speed, (2) high-fidelity tracking of controller-invariant motion targets, (3) adaptive speed modulation that dynamically adjusts execution speed based on motion complexity, and (4) action scheduling to handle real-world system latencies. Experiments on 12 tasks across simulation and two real, distinct robot platforms shows that SAIL achieves up to a {4$\times$ speedup} over demonstration speed in simulation and up to {3.2$\times$ speedup} in the real world. Additional detail is available at https://sail-robot.github.io} }
Endnote
%0 Conference Paper %T SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies %A Nadun Ranawaka Arachchige %A Zhenyang Chen %A Wonsuhk Jung %A Woo Chul Shin %A Rohan Bansal %A Pierre Barroso %A Yu Hang He %A Yingyan Celine Lin %A Benjamin Joffe %A Shreyas Kousik %A Danfei Xu %B Proceedings of The 9th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Joseph Lim %E Shuran Song %E Hae-Won Park %F pmlr-v305-arachchige25a %I PMLR %P 721--749 %U https://proceedings.mlr.press/v305/arachchige25a.html %V 305 %X Offline Imitation Learning (IL) methods such as Behavior Cloning are effective at acquiring complex robotic manipulation skills. However, existing IL-trained policies are confined to execute the task at the same speed as shown in demonstration data. This limits the task throughput of a robotic system, a critical requirement for applications such as industrial automation. In this paper, we introduce and formalize the novel problem of enabling faster-than-demonstration execution of visuomotor policies and identify fundamental challenges in robot dynamics and state-action distribution shifts. We instantiate the key insights as SAIL (Speed Adaptation for Imitation Learning), a full-stack system integrating four tightly-connected components: (1) a consistency-preserving action inference algorithm for smooth motion at high speed, (2) high-fidelity tracking of controller-invariant motion targets, (3) adaptive speed modulation that dynamically adjusts execution speed based on motion complexity, and (4) action scheduling to handle real-world system latencies. Experiments on 12 tasks across simulation and two real, distinct robot platforms shows that SAIL achieves up to a {4$\times$ speedup} over demonstration speed in simulation and up to {3.2$\times$ speedup} in the real world. Additional detail is available at https://sail-robot.github.io
APA
Arachchige, N.R., Chen, Z., Jung, W., Shin, W.C., Bansal, R., Barroso, P., He, Y.H., Lin, Y.C., Joffe, B., Kousik, S. & Xu, D.. (2025). SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies. Proceedings of The 9th Conference on Robot Learning, in Proceedings of Machine Learning Research 305:721-749 Available from https://proceedings.mlr.press/v305/arachchige25a.html.

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