2026-03-19 ジョージア工科大学
Pancake-flipping robots could be just around the corner thanks to a new robot learning system from Georgia Tech. (Credit: Adobe Stock)
＜関連情報＞
- https://research.gatech.edu/smarter-faster-and-more-human-leap-toward-general-purpose-robots
- https://arxiv.org/abs/2506.11948
SAIL：模倣学習ポリシーのデモンストレーションよりも高速な実行 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, Yingyang Celine Lin, Benjamin Joffe, Shreyas Kousik, Danfei Xu
arXiv last revised 8 Sep 2025 (this version, v2)
DOI:https://doi.org/10.48550/arXiv.2506.11948
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 executing 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 show that SAIL achieves up to a 4x speedup over demonstration speed in simulation and up to 3.2x speedup in the real world.
