Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network

In robot control with physical interaction, like robot-assisted surgery and bilateral teleoperation, the availability of reliable interaction force information has proved to be capable of increasing the control precision and of dealing with the surrounding complex environments. Usually, force sensors are mounted between the end effector of the robot manipulator and the tool for measuring the interaction forces on the tooltip. In this case, the force acquired from the force sensor includes not only the interaction force but also the gravity force of the tool. Hence the tool dynamic identification is required for accurate dynamic simulation and model-based control. Although model-based techniques have already been widely used in traditional robotic arms control, their accuracy is limited due to the lack of specific dynamic models. This work proposes a model-free technique for dynamic identification using multi-layer neural networks (MNN). It utilizes two types of MNN architectures based on both feed-forward networks (FF-MNN) and cascade-forward networks (CF-MNN) to model the tool dynamics. Compared with the model-based technique, i.e., curve fitting (CF), the accuracy of the tool identification is improved. After the identification and calibration, a further demonstration of bilateral teleoperation is presented using a serial robot (LWR4+, KUKA, Germany) and a haptic manipulator (SIGMA 7, Force Dimension, Switzerland). Results demonstrate the promising performance of the model-free tool identification technique using MNN, improving the results provided by model-based methods.

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Model Predictive Torque Control for Velocity Tracking of a Four-Wheeled Climbing Robot

Sensors ◽

10.3390/s20247059 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7059

Author(s):

Higor Barbosa Santos ◽

Marco Antonio Simoes Teixeira ◽

Nicolas Dalmedico ◽

Andre Schneider de Oliveira ◽

Flavio Neves-Jr ◽

...

Keyword(s):

Dynamic Models ◽

Nonlinear Effects ◽

Interaction Force ◽

Torque Control ◽

Climbing Robot ◽

Kinematic Modeling ◽

Model Based ◽

Climbing Ability ◽

Nonlinear Dynamic Models ◽

Velocity Tracking

Climbing robots are characterized by a secure surface coupling that is designed to prevent falling. The robot coupling ability is assured by an adhesion method leading to nonlinear dynamic models with time-varying parameters that affect the robot’s mobility. Additionally, the wheel friction and the force of gravity force are also relevant issues that can compromise the climbing ability if they are not well modeled. This work presents a model-based torque controller for velocity tracking in a four-wheeled climbing robot specially designed to inspect storage tanks. The model-based controller (MPC) compensates for the effects of nonlinearities due to the forces of gravity, friction, and adhesion through the dynamic and kinematic modeling of the climbing robot. Dynamic modeling is based on the Lagrange-Euler approach, which allows a better understanding of how forces and torques affect the robot’s movement. Besides, an analysis of the interaction force between the robot and the contact surface is proposed, since this force affects the motion of the climbing robot according to spatial orientation. Finally, simulations are carried out to examine the robot’s dynamics during the climbing movement, and the MPC is validated through the redrobot simulator V-REP and practical experiments. The presented results highlight the compensation of the nonlinear effects due to the robot’s climbing motion by the proposed MPC controller.

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Robot Manipulator Calibration Using a Model Based Identification Technique and a Neural Network With the Teaching Learning-Based Optimization

IEEE Access ◽

10.1109/access.2020.2999927 ◽

2020 ◽

Vol 8 ◽

pp. 105447-105454 ◽

Cited By ~ 1

Author(s):

Phu-Nguyen Le ◽

Hee-Jun Kang

Keyword(s):

Neural Network ◽

Robot Manipulator ◽

Model Based ◽

Identification Technique ◽

Teaching Learning Based Optimization ◽

Teaching Learning

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Differential Soft Sensor-Based Measurement of Interactive Force and Assistive Torque for a Robotic Hip Exoskeleton

Sensors ◽

10.3390/s21196545 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6545

Author(s):

Sun’an Wang ◽

Binquan Zhang ◽

Zhenyuan Yu ◽

Yu’ang Yan

Keyword(s):

Human Body ◽

Force Measurement ◽

Low Cost ◽

Wearable Sensors ◽

Force Sensor ◽

Interaction Force ◽

Soft Sensor ◽

Sensor System ◽

Physical Interaction ◽

Indirect Measure

With the emerging of wearable robots, the safety and effectiveness of human-robot physical interaction have attracted extensive attention. Recent studies suggest that online measurement of the interaction force between the robot and the human body is essential to the aspects above in wearable exoskeletons. However, a large proportion of existing wearable exoskeletons monitor and sense the delivered force and torque through an indirect-measure method, in which the torque is estimated by the motor current. Direct force/torque measuring through low-cost and compact wearable sensors remains an open problem. This paper presents a compact soft sensor system for wearable gait assistance exoskeletons. The contact force is converted into a voltage signal by measuring the air pressure within a soft pneumatic chamber. The developed soft force sensor system was implemented on a robotic hip exoskeleton, and the real-time interaction force between the human thigh and the exoskeleton was measured through two differential soft chambers. The delivered torque of the hip exoskeleton was calculated based on a characterization model. Experimental results suggested that the sensor system achieved direct force measurement with an error of 10.3 ± 6.58%, and torque monitoring for a hip exoskeleton which provided an understanding for the importance of direct force/torque measurement for assistive performance. Compared with traditional rigid force sensors, the proposed system has several merits, as it is compact, low-cost, and has good adaptability to the human body due to the soft structure.

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Shaping Model-Free Reinforcement-Learning with Model-Based Pseudorewards

10.32470/ccn.2018.1191-0 ◽

2018 ◽

Author(s):

Paul Krueger ◽

Thomas Griffiths

Keyword(s):

Reinforcement Learning ◽

Model Based ◽

Model Free

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Model-Based and Model-Free Social Cognition

10.31234/osf.io/ue6j2 ◽

2019 ◽

Author(s):

Leor M Hackel ◽

Jeffrey Jordan Berg ◽

Björn Lindström ◽

David Amodio

Keyword(s):

Reinforcement Learning ◽

Social Cognition ◽

Learning Strategies ◽

Memory Systems ◽

Learning Task ◽

Financial Advisors ◽

Model Based ◽

Model Free ◽

Systems Model ◽

Task Assessment

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions—computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each independently predicted choice during the learning task and self-reported liking in a post-task assessment. Specifically, participants liked advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Moreover, participants varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

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Faculty Opinions recommendation of States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.4125957.4076054 ◽

2010 ◽

Author(s):

Susan Courtney

Keyword(s):

Reinforcement Learning ◽

Prediction Error ◽

Model Based ◽

Model Free

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Author Correction: Reliance on model-based and model-free control in obesity

Scientific Reports ◽

10.1038/s41598-021-83028-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lieneke K. Janssen ◽

Florian P. Mahner ◽

Florian Schlagenhauf ◽

Lorenz Deserno ◽

Annette Horstmann

Keyword(s):

Model Based ◽

Model Free ◽

Model Free Control

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace

Sensors ◽

10.3390/s21051797 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1797

Author(s):

Ján Vachálek ◽

Dana Šišmišová ◽

Pavol Vašek ◽

Jan Rybář ◽

Juraj Slovák ◽

...

Keyword(s):

Machine Learning ◽

Control Charts ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Conveyor Belt ◽

Standard Uncertainty ◽

K Nearest Neighbor ◽

Industrial Products ◽

Dynamic Identification ◽

Identification Technique

The article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with a conveyor belt, robot and an industrial color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form of type A standard uncertainty and type B standard uncertainty, in order to obtain combined standard uncertainties results. Based on the acquired data, control charts of RGB color components for identified products are created. Influence of product speed on the measuring process identification and process stability is monitored. In case of identification uncertainty i.e., measured values are outside the limits of control charts, the K-nearest neighbor machine learning algorithm is used. This algorithm, based on the Euclidean distances to the classified value, estimates its most accurate iteration. This results into the comprehensive system for identification of product moving on conveyor belt, where based on the data collection and statistical analysis using machine learning, industry usage reliability is demonstrated.

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