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Modelling and Learning Dynamics for Robotic Food-Cutting

Mitsioni, Ioanna ; Karayiannidis, Yiannis LU orcid and Kragic, Danica (2021) p.1194-1200
Abstract
Interaction dynamics are difficult to model analytically, making data-driven controllers preferable for contact-rich manipulation tasks. In this work, we approximate the intricate dynamics of food-cutting with a Long Short-Term Memory (LSTM) model to apply a Model Predictive Controller (MPC). We propose a problem formulation that allows velocity-controlled robots to learn the interaction dynamics and tackle the difficulty of multi-step predictions by training the model with a horizon curriculum. We experimentally demonstrate that our approach leads to good predictive performance that scales for longer prediction horizons, generalizes to unseen object classes and results in controller behaviors with an understanding of the cutting dynamics.
Please use this url to cite or link to this publication:
author
; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)
pages
7 pages
external identifiers
  • scopus:85117040503
DOI
10.1109/CASE49439.2021.9551558
language
English
LU publication?
no
id
f3f8a772-e6d5-4dec-b4e0-60b2ea2f7065
date added to LUP
2022-12-14 15:07:36
date last changed
2025-04-04 14:44:01
@inproceedings{f3f8a772-e6d5-4dec-b4e0-60b2ea2f7065,
  abstract     = {{Interaction dynamics are difficult to model analytically, making data-driven controllers preferable for contact-rich manipulation tasks. In this work, we approximate the intricate dynamics of food-cutting with a Long Short-Term Memory (LSTM) model to apply a Model Predictive Controller (MPC). We propose a problem formulation that allows velocity-controlled robots to learn the interaction dynamics and tackle the difficulty of multi-step predictions by training the model with a horizon curriculum. We experimentally demonstrate that our approach leads to good predictive performance that scales for longer prediction horizons, generalizes to unseen object classes and results in controller behaviors with an understanding of the cutting dynamics.}},
  author       = {{Mitsioni, Ioanna and Karayiannidis, Yiannis and Kragic, Danica}},
  booktitle    = {{2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)}},
  language     = {{eng}},
  pages        = {{1194--1200}},
  title        = {{Modelling and Learning Dynamics for Robotic Food-Cutting}},
  url          = {{http://dx.doi.org/10.1109/CASE49439.2021.9551558}},
  doi          = {{10.1109/CASE49439.2021.9551558}},
  year         = {{2021}},
}