TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning

Jimmy Wu, William Chong, Robert Holmberg, Aaditya Prasad, Yihuai Gao, Oussama Khatib, Shuran Song, Szymon Rusinkiewicz, Jeannette Bohg

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Exploiting the promise of recent advances in imitation learning for mobile manipulation will require the collection of large numbers of human-guided demonstrations. This paper proposes an open-source design for an inexpensive, robust, and flexible mobile manipulator that can support arbitrary arms, enabling a wide range of real-world household mobile manipulation tasks. Crucially, our design uses powered casters to enable the mobile base to be fully holonomic, able to control all planar degrees of freedom independently and simultaneously. This feature makes the base more maneuverable and simplifies many mobile manipulation tasks, eliminating the kinematic constraints that create complex and time-consuming motions in nonholonomic bases. We equip our robot with an intuitive mobile phone teleoperation interface to enable easy data acquisition for imitation learning. In our experiments, we use this interface to collect data and show that the resulting learned policies can successfully perform a variety of common household mobile manipulation tasks.

Original languageEnglish (US)
Pages (from-to)3729-3741
Number of pages13
JournalProceedings of Machine Learning Research
Volume270
StatePublished - 2024
Event8th Conference on Robot Learning, CoRL 2024 - Munich, Germany
Duration: Nov 6 2024Nov 9 2024

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

Keywords

  • holonomic drive
  • imitation learning
  • mobile manipulation

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