Universal Manipulation Interface: Enabling Dynamic Robot Teaching Without In-The-Wild Robots

Core Concepts

Universal Manipulation Interface (UMI) enables direct skill transfer from in-the-wild human demonstrations to deployable robot policies, unlocking new robot manipulation capabilities.

Abstract

Universal Manipulation Interface (UMI) is a portable, intuitive, low-cost data collection and policy learning framework that allows for the transfer of diverse human demonstrations to effective visuomotor policies. UMI addresses critical issues such as insufficient visual context, action imprecision, latency discrepancies, and insufficient policy representation in previous works. By carefully designing the demonstration and policy interface, UMI provides a practical and accessible framework for teaching robots complex manipulation skills. The system captures rapid movements with absolute scale using IMU-aware tracking and continuous gripper control. UMI also incorporates kinematic-based data filtering to ensure valid trajectories for different robot embodiments. Through experiments on tasks like cup arrangement, dynamic tossing, bimanual cloth folding, and dish washing, UMI demonstrates its capability to handle various manipulation challenges with high success rates. Additionally, UMI showcases strong generalization capabilities to novel environments and objects through in-the-wild data collection. The system offers improved data collection throughput compared to traditional teleoperation methods while maintaining high accuracy in SLAM-based tracking.

Stats

UMI achieves 87.5% success rate in dynamic tossing task.
The system achieves 70% success rate in bimanual cloth folding task.
UMI demonstrates 70% success rate in dish washing task.

Quotes

"UMI eliminates the need for physical robots during data collection and offers a more portable interface for in-the-wild robot teaching."
"We demonstrate UMI’s versatility and efficacy with comprehensive real-world experiments."
"UMI's hardware and software system is open-sourced at https://umi-gripper.github.io."

Key Insights Distilled From

Universal Manipulation Interface

by Cheng Chi,Zh... at arxiv.org 03-07-2024

https://arxiv.org/pdf/2402.10329.pdf

Deeper Inquiries

どのようにしてUMIを、精度を損なうことなくデータ収集スループットを向上させるためにさらに最適化できますか？

UMIのデータ収集スループットを向上させるためには、いくつかの方法が考えられます。まず第一に、UMIグリッパーの軽量化や操作性の改善が重要です。軽量で取り回しやすい材料や設計変更を行うことで、ユーザーが効率的にデモンストレーションを行えるようにすることがポイントです。また、デモンストレーションから学習されたポリシーがロボットアームの制約条件内であることを確認するフィルタリングプロセスも効果的です。これにより、無駄な動作や不適切なアクションが削減され、学習プロセス全体が迅速化されます。