Data–Driven Disturbance Observers for Estimating External Forces on Soft Robots
Name
09145617.pdf
Description
Published version
Size
3 MB
Format
Adobe PDF
Checksum (MD5)
b1642934c03d01924bbe037288a0758f
Author(s)
Santina, Cosimo Della
Truby, Ryan Landon
Rus, Daniela
Date Issued
2020
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Final published version
Abstract
© 2016 IEEE. Unlike traditional robots, soft robots can intrinsically interact with their environment in a continuous, robust, and safe manner. These abilities - and the new opportunities they open - motivate the development of algorithms that provide reliable information on the nature of environmental interactions and, thereby, enable soft robots to reason on and properly react to external contact events. However, directly extracting such information with integrated sensors remains an arduous task that is further complicated by also needing to sense the soft robot's configuration. As an alternative to direct sensing, this paper addresses the challenge of estimating contact forces directly from the robot's posture. We propose a new technique that merges a nominal disturbance observer, a model-based component, with corrections learned from data. The result is an algorithm that is accurate yet sample efficient, and one that can reliably estimate external contact events with the environment. We prove the convergence of our proposed method analytically, and we demonstrate its performance with simulations and physical experiments.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/LRA.2020.3010738
