Ep#59: SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies

Teleoperating a robot is hard. This means that when performing a robot task via teleoperation — say, to collect examples for training a robot policy — it’s almost unavoidably slower than you would like, below either the capabilities of the human expert on their own or the robot performing the task. Wouldn’t it be great if there was a way to fix this?

Unfortunately, it’s harder than it looks. You can’t just execute faster, as this alters the distribution of environment states the policy will encounter. Nadun Ranakawa Arachchige and Zhenyang Chen propose Speed-Adaptive Imitation Learning (SAIL), which adds error-adaptive guidance, adapts execution speed according to task structure, predicts controller-invariant action targets to ensure robustness across execution speeds, and explicitly models delays from, for example, sensor latency.

Watch episode #59 of RoboPapers, with Chris Paxton and Michael Cho to learn more!

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. Additional detail is available at this https URL

Project site: https://nadunranawaka1.github.io/sail-policy/

ArXiV: https://arxiv.org/abs/2506.11948

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