Modelling and Learning Dynamics for Robotic Food-Cutting
Paper i proceeding, 2021

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.


Ioanna Mitsioni

Kungliga Tekniska Högskolan (KTH)

Yiannis Karayiannidis

Chalmers, Elektroteknik, System- och reglerteknik

Danica Kragic

Kungliga Tekniska Högskolan (KTH)

IEEE International Conference on Automation Science and Engineering

21618070 (ISSN) 21618089 (eISSN)

Vol. 2021-August 1194-1200
9781665418737 (ISBN)

17th IEEE International Conference on Automation Science and Engineering, CASE 2021
Lyon, France,


Robotteknik och automation


Datavetenskap (datalogi)



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