# EgoSpeedUp: Transferring Human Manipulation Tempo to Robot Policies

arXiv: https://arxiv.org/abs/2609.29310 · HTML: https://mlsanity.com/paper/2609.29310/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 21 of its 26 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## 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, 21 of 21)

- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [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)
- [R3M: A Universal Visual Representation for Robot Manipulation](https://mlsanity.com/paper/2203.12601/) (2022)
- [Robotic Control via Embodied Chain-of-Thought Reasoning](https://mlsanity.com/paper/2407.08693/) (2024)
- [EgoMimic: Scaling Imitation Learning via Egocentric Video](https://mlsanity.com/paper/2410.24221/) (2024)
- [Human-to-Robot Imitation in the Wild](https://mlsanity.com/paper/2207.09450/) (2022)
- [Flow as the Cross-Domain Manipulation Interface](https://mlsanity.com/paper/2407.15208/) (2024)
- [WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild](https://mlsanity.com/paper/2409.12259/) (2024)
- [EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data](https://mlsanity.com/paper/2602.16710/) (2026)
- [EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World](https://mlsanity.com/paper/2604.07607/) (2026)
- [ManiFlow: A General Robot Manipulation Policy via Consistency Flow Training](https://mlsanity.com/paper/2509.01819/) (2025)
- [Emergence of Human to Robot Transfer in Vision-Language-Action Models](https://mlsanity.com/paper/2512.22414/) (2025)
- [EgoBridge: Domain Adaptation for Generalizable Imitation from Egocentric Human Data](https://mlsanity.com/paper/2509.19626/) (2025)
- [ImMimic: Cross-Domain Imitation from Human Videos via Mapping and Interpolation](https://mlsanity.com/paper/2509.10952/) (2025)
- [SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies](https://mlsanity.com/paper/2506.11948/) (2025)
- [DemoSpeedup: Accelerating Visuomotor Policies via Entropy-Guided Demonstration Acceleration](https://mlsanity.com/paper/2506.05064/) (2025)
- [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)
- [VOLT: Vision and Language Trajectory Segmentation for Faster-than-Demonstration Policies](https://mlsanity.com/paper/2606.06323/) (2026)
- [AutoSpeed: Annotation-Free Stage-Adaptive Motion Speed Learning for Robot Manipulation](https://mlsanity.com/paper/2607.01051/) (2026)

## Abstract (arXiv)

Robot manipulation policies trained through imitation learning inherit not only the demonstrated behavior but also the conservative execution tempo of robot demonstrations. Existing acceleration approaches can execute faster than the original demonstrations, but determine the appropriate acceleration primarily from robot-side information or a predefined set of tempo factors, leaving open how to obtain a task-appropriate reference for how fast each manipulation phase should progress. We introduce EgoSpeedUp, a framework that uses human manipulation as temporal supervision for robot imitation learning. Our key insight is that human demonstrations naturally reveal task-appropriate, phase-wise manipulation tempo. Given slow robot demonstrations and human demonstrations of the same task, EgoSpeedUp aligns corresponding manipulation phases, estimates their relative execution tempos from multiple human demonstrations, and transfers the resulting phase-wise tempo by retiming the robot demonstrations. The retimed demonstrations are then used for standard behavior cloning, allowing the robot to retain its executable manipulation behavior while learning to perform it at a human-informed tempo. Across two real-world manipulation tasks, EgoSpeedUp improves the task success rate by an average of 25 percentage points (pp) while reducing successful execution time by 36.5%. These results demonstrate that human manipulation tempo provides an effective temporal reference for learning faster and more reliable robot policies.