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.

Författare

Ioanna Mitsioni

Kungliga Tekniska Högskolan (KTH)

Yiannis Karayiannidis

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

Danica Kragic

Kungliga Tekniska Högskolan (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,

Ämneskategorier

Robotteknik och automation

Reglerteknik

Datavetenskap (datalogi)

DOI

10.1109/CASE49439.2021.9551558

Mer information

Senast uppdaterat

2021-11-01