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

arXiv: https://arxiv.org/abs/2506.11948 · HTML: https://mlsanity.com/paper/2506.11948/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 25 of its 47 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 25 of 25)

- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024)
- [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://mlsanity.com/paper/2106.02039/) (2021)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations](https://mlsanity.com/paper/2310.17596/) (2023)
- [Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations](https://mlsanity.com/paper/1904.06387/) (2019)
- [Open-TeleVision: Teleoperation with Immersive Active Visual Feedback](https://mlsanity.com/paper/2407.01512/) (2024)
- [VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors](https://mlsanity.com/paper/2210.11339/) (2022)
- [Imitation Learning from Imperfect Demonstration](https://mlsanity.com/paper/1901.09387/) (2019)
- [OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation](https://mlsanity.com/paper/2403.07870/) (2024)
- [Waypoint-Based Imitation Learning for Robotic Manipulation](https://mlsanity.com/paper/2307.14326/) (2023)
- [Neural Dynamic Policies for End-to-End Sensorimotor Learning](https://mlsanity.com/paper/2012.02788/) (2020)
- [Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion Models](https://mlsanity.com/paper/2406.04806/) (2024)
- [Dynamic Movement Primitives in Robotics: A Tutorial Survey](https://mlsanity.com/paper/2102.03861/) (2021)
- [Riemannian Motion Policies](https://mlsanity.com/paper/1801.02854/) (2018)
- [Hierarchical Neural Dynamic Policies](https://mlsanity.com/paper/2107.05627/) (2021)
- [Imitation Learning for Variable Speed Contact Motion for Operation up to Control Bandwidth](https://mlsanity.com/paper/2102.10283/) (2021)
- [Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed](https://mlsanity.com/paper/2206.10289/) (2022)
- [What's the Move? Hybrid Imitation Learning via Salient Points](https://mlsanity.com/paper/2412.05426/) (2024)
- [Imitation learning for variable speed motion generation over multiple actions](https://mlsanity.com/paper/2103.06466/) (2021)

## Built on by (most cited first, 18 of 18)

- [ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations](https://mlsanity.com/paper/2510.01607/) (2025)
- [Learning Native Continuation for Action Chunking Flow Policies](https://mlsanity.com/paper/2602.12978/) (2026)
- [Tune to Learn: How Controller Gains Shape Robot Policy Learning](https://mlsanity.com/paper/2604.02523/) (2026)
- [SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning](https://mlsanity.com/paper/2512.00062/) (2025)
- [ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning](https://mlsanity.com/paper/2512.07371/) (2025)
- [Speedup Patch: Learning a Plug-and-Play Policy to Accelerate Embodied Manipulation](https://mlsanity.com/paper/2603.20658/) (2026)
- [TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies](https://mlsanity.com/paper/2606.06491/) (2026)
- [From Inference Efficiency to Embodied Efficiency: Revisiting Efficiency Metrics for Vision-Language-Action Models](https://mlsanity.com/paper/2603.19131/) (2026)
- [Flip Stunts on Bicycle Robots using Iterative Motion Imitation](https://mlsanity.com/paper/2603.27944/) (2026)
- [When a Robot is More Capable than a Human: Learning from Constrained Demonstrators](https://mlsanity.com/paper/2510.09096/) (2025)
- [Habilis-β: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model](https://mlsanity.com/paper/2602.18813/) (2026)
- [Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning](https://mlsanity.com/paper/2604.16850/) (2026)
- [OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies](https://mlsanity.com/paper/2607.03723/) (2026)
- [Device-Conditioned Neural Architecture Search for Efficient Robotic Manipulation](https://mlsanity.com/paper/2604.10170/) (2026)
- [SkiP: When to Skip and When to Refine for Efficient Robot Manipulation](https://mlsanity.com/paper/2605.15536/) (2026)
- [LAGO Policy: Latency-Aware Asynchronous Diffusion Policies with Goal-Directed Collision-Free Planning for Smooth Manipulation](https://mlsanity.com/paper/2606.17982/) (2026)
- [Does Imitation Learning Preserve Temporal Robustness in Dexterous Manipulation? An Expert-Learner Comparison Across Task Execution Speeds](https://mlsanity.com/paper/2609.01453/) (2026)
- [EgoSpeedUp: Transferring Human Manipulation Tempo to Robot Policies](https://mlsanity.com/paper/2609.29310/) (2026)

## Abstract (arXiv)

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