EasyMimic: A Low-Cost Framework for Robot Imitation Learning from Human Videos

TL;DR

EasyMimic is a low-cost, user-friendly framework that enables robots to learn manipulation tasks from human videos using RGB cameras, reducing the need for expensive data collection.

Contribution

The paper introduces EasyMimic, a novel framework that leverages human video demonstrations and simple augmentation to facilitate robot imitation learning on low-cost platforms.

Findings

Achieves high performance on manipulation tasks with minimal robot data

Effectively bridges human-robot domain gap with visual augmentation

Reduces data collection costs significantly

Abstract

Robot imitation learning is often hindered by the high cost of collecting large-scale, real-world data. This challenge is especially significant for low-cost robots designed for home use, as they must be both user-friendly and affordable. To address this, we propose the EasyMimic framework, a low-cost and replicable solution that enables robots to quickly learn manipulation policies from human video demonstrations captured with standard RGB cameras. Our method first extracts 3D hand trajectories from the videos. An action alignment module then maps these trajectories to the gripper control space of a low-cost robot. To bridge the human-to-robot domain gap, we introduce a simple and user-friendly hand visual augmentation strategy. We then use a co-training method, fine-tuning a model on both the processed human data and a small amount of robot data, enabling rapid adaptation to new tasks. Experiments on the low-cost LeRobot platform demonstrate that EasyMimic achieves high performance across various manipulation tasks. It significantly reduces the reliance on expensive robot data collection, offering a practical path for bringing intelligent robots into homes. Project website: https://zt375356.github.io/EasyMimic-Project/.