軌跡をスケッチしてロボットに動きを教える(Teaching Robots To Move by Sketching Trajectories)

ad

2024-05-07 カーネギーメロン大学

カーネギーメロン大学の研究チームは、ロボットに動きの軌跡を教える新しい方法を開発しています。この方法では、環境の画像に動きの軌跡をスケッチし、それを3Dモデルに変換してロボットが理解できる形で提供します。これにより、ロボットは様々なタスクを正確に実行できるようになります。この技術は、従来の運動教示方法の欠点を克服し、プログラミングが不得意な人でもロボットと協力して作業を行える可能性を秘めています。

<関連情報>

軌跡をスケッチすることでロボットに動きを教える:確率的図解教育によるデモンストレーションからの学習 Instructing Robots by Sketching: Learning from Demonstration via Probabilistic Diagrammatic Teaching

Weiming Zhi, Tianyi Zhang, Matthew Johnson-Roberson
arxiv  31 Mar 2024

軌跡をスケッチしてロボットに動きを教える(Teaching Robots To Move by Sketching Trajectories)

Abstract

Learning from Demonstration (LfD) enables robots to acquire new skills by imitating expert demonstrations, allowing users to communicate their instructions intuitively. Recent progress in LfD often relies on kinesthetic teaching or teleoperation as the medium for users to specify the demonstrations. Kinesthetic teaching requires physical handling of the robot, while teleoperation demands proficiency with additional hardware. This paper introduces an alternative paradigm for LfD called Diagrammatic Teaching. Diagrammatic Teaching aims to teach robots novel skills by prompting the user to sketch out demonstration trajectories on 2D images of the scene, these are then synthesised as a generative model of motion trajectories in 3D task space. Additionally, we present the Ray-tracing Probabilistic Trajectory Learning (RPTL) framework for Diagrammatic Teaching. RPTL extracts time-varying probability densities from the 2D sketches, then applies ray-tracing to find corresponding regions in 3D Cartesian space, and fits a probabilistic model of motion trajectories to these regions. New motion trajectories, which mimic those sketched by the user, can then be generated from the probabilistic model. We empirically validate our framework both in simulation and on real robots, which include a fixed-base manipulator and a quadruped-mounted manipulator.

0109ロボット
ad
ad
Follow
ad
タイトルとURLをコピーしました