Title
Fast Haptic Terrain Classification for Legged Robots Using Transformer
Authors
[ 1 ] Instytut Robotyki i Inteligencji Maszynowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ 2 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ D ] phd student | [ P ] employee
Scientific discipline (Law 2.0)
[2.2] Automation, electronics and electrical engineering[2.3] Information and communication technology
Year of publication
2021
Chapter type
chapter in monograph / paper
Publication language
english
Abstract
EN The haptic terrain classification is an essential component of a mobile walking robot control system, ensuring proper gait adaptation to the changing environmental conditions. In this work, we further tackle this problem with force and torque measurements from feet while focusing on real-life applicability defined as low computational demand and rapid inference time. To meet these requirements, we propose two classical machine learning algorithms (DTW-KNN and ROCKET) and two deep-learning solutions – a typical feed-forward solution based on temporal convolution network (TCN) and the currently prevailing transformer architecture. The experiments conducted on the publicly available haptic classification dataset revealed that we could reach classification results marginally lower than state of the art with networks containing up to 50 times fewer parameters within an improved inference time of several milliseconds on a CPU.
Date of online publication
21.10.2021
Pages (from - to)
1 - 7
Ministry points / chapter
20
System created by Poznań University of Technology
and Poznan Supercomputing and Networking Center
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