GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policy

概要

Due to the inherent uncertainty in their deformability during motion, previous methods in rope manipulation often require hundreds of real-world demonstrations to train a manipulation policy for each rope, even for simple tasks such as rope goal reaching, which hinder their applications in our ever-changing world. To address this issue, we introduce GenORM, a framework that allows the manipulation policy to handle different deformable ropes with a single real-world demonstration. To achieve this, we augment the policy by conditioning it on deformable rope parameters and training it with a diverse range of simulated deformable ropes so that the policy can adjust actions based on different rope parameters. At the time of inference, given a new rope, GenORM estimates the deformable rope parameters by minimizing the disparity between the grid density of point clouds of real-world demonstrations and simulations. With the help of a differentiable physics simulator, we require only a single real-world demonstration. Empirical validations on both simulated and real-world rope manipulation setups clearly show that our method can manipulate different ropes with a single demonstration and significantly outperforms the baseline in both environments (62% improvement in in-domain ropes, and 15% improvement in out-of-distribution ropes in simulation, 26% improvement in real-world), demonstrating the effectiveness of our approach in one-shot rope manipulation.

タイプ
松嶋 達也
松嶋 達也
特任研究員

人間と共生できるような適応的なロボットの開発と,そのようなロボットを作ることにより生命性や知能を構成的に理解することに興味があります.

大久保 拓哉
大久保 拓哉
学部生

ロボット・制御工学・数理工学に興味があります。

池田 悠也
池田 悠也
修士課程

工学部システム創成学科知能社会システムコース4年

高波 亮介
高波 亮介
修士課程