Data-Driven Disturbance Observers for Estimating External Forces on Soft Robots

作     者：Santina, Cosimo Della Truby, Ryan Landon Rus, Daniela 

作者机构：MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA 

出 版 物：《IEEE ROBOTICS AND AUTOMATION LETTERS》 (IEEE Robot. Autom.)

年 卷 期：2020年第5卷第4期

页      面：5717-5724页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 

基　　金：NSF EFRI Program Schmidt Science Fellows program Rhodes Trust 

主　　题：Modeling control and learning for soft robots contact modeling model learning for control 

摘      要：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.