Dynamic Manipulation of Deformable Objects using Imitation Learning with Adaptation to Hardware Constraints

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

Imitation Learning (IL) is a promising paradigm for learning dynamic manipulation of deformable objects since it does not depend on difficult-to-create accurate simulations of such objects. However, the translation of motions demonstrated by a human to a robot is a challenge for IL, due to differences in the embodiments and the robot’s physical limits. These limits are especially relevant in dynamic manipulation where high velocities and accelerations are typical. To address this problem, we propose a framework that first maps a dynamic demonstration into a motion that respects the robot’s constraints using a constrained Dynamic Movement Primitive. Second, the resulting object state is further optimized by quasi-static refinement motions to optimize task performance metrics. This allows both efficiently altering the object state by dynamic motions and stable small-scale refinements. We evaluate the framework in the challenging task of bag opening, designing the system BILBO: Bimanual dynamic manipulation using Imitation Learning for Bag Opening. Our results show that BILBO can successfully open a wide range of crumpled bags, using a demonstration with a single bag. See supplementary material at https://sites.google.com/view/bilbo-bag.
Original languageEnglish
Title of host publication2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages12655-12662
Number of pages8
ISBN (Electronic)979-8-3503-7770-5
DOIs
Publication statusPublished - 2024
MoE publication typeA4 Conference publication
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - ADNEC, Abu Dhabi, United Arab Emirates
Duration: 14 Oct 202418 Oct 2024
https://iros2024-abudhabi.org/

Publication series

NameProceedings of the International Conference on Intelligent Robots and Systems
ISSN (Electronic)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period14/10/202418/10/2024
Internet address

Keywords

  • Imitation Learning
  • Dynamic Manipulation
  • Intelligent robots

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