Modelling and Learning Dynamics for Robotic Food-Cutting
Paper in 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.

Author

Ioanna Mitsioni

Royal Institute of Technology (KTH)

Yiannis Karayiannidis

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Danica Kragic

Royal Institute of Technology (KTH)

IEEE International Conference on Automation Science and Engineering

21618070 (ISSN) 21618089 (eISSN)

Vol. 2021-August 1194-1200

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

Subject Categories

Robotics

Control Engineering

Computer Science

DOI

10.1109/CASE49439.2021.9551558

More information

Latest update

11/1/2021