Filters








433 Hits in 6.5 sec

Vision-based Online Learning Kinematic Control for Soft Robots using Local Gaussian Process Regression

Ge Fang, Xiaomei Wang, Kui Wang, Kit-Hang Lee, Justin Di-Lang Ho, Hing Choi Fu, Kin Chung Denny Fu, Ka-Wai Kwok
2019 IEEE Robotics and Automation Letters  
Index Terms-Eye-in-hand visual-servo, Learning-based control, Local Gaussian process regression, Soft robot control.  ...  Local Gaussian process regression (GPR) is used to initialize and refine the inverse mappings online, without prior knowledge of robot and camera parameters.  ...  Vision-based Online Learning Kinematic Control for Soft Robots using Local Gaussian Process Regression In this paper, we propose an adaptive eye-in-hand visual servo control framework based on local online  ... 
doi:10.1109/lra.2019.2893691 fatcat:cifzcd3xwbc77drnd3viekjmai

A Survey for Machine Learning-Based Control of Continuum Robots

Xiaomei Wang, Yingqi Li, Ka-Wai Kwok
2021 Frontiers in Robotics and AI  
To this end, data-driven modeling strategies making use of machine learning algorithms would be an encouraging way out for the control of soft continuum robots.  ...  In this article, we attempt to overview the current state of kinematic/dynamic model-free control schemes for continuum manipulators, particularly by learning-based means, and discuss their similarities  ...  2018) and (locally) Gaussian process regression (GPR) (Fang et al., 2019) .  ... 
doi:10.3389/frobt.2021.730330 pmid:34692777 pmcid:PMC8527450 fatcat:p4yeo5jqajfhphzsdbiu746swa

Review of machine learning methods in soft robotics

Daekyum Kim, Sang-Hun Kim, Taekyoung Kim, Brian Byunghyun Kang, Minhyuk Lee, Wookeun Park, Subyeong Ku, DongWook Kim, Junghan Kwon, Hochang Lee, Joonbum Bae, Yong-Lae Park (+2 others)
2021 PLoS ONE  
followed by a summary of the existing machine learning methods for soft robots.  ...  However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity  ...  Kim used a Gaussian Process Regression to learn control policy for a simple tripod mobile robot based on membrane vibration actuators [90] . M.  ... 
doi:10.1371/journal.pone.0246102 pmid:33600496 pmcid:PMC7891779 fatcat:alu4zm72irespj6wydikzjb6ie

2020 Index IEEE Robotics and Automation Letters Vol. 5

2020 IEEE Robotics and Automation Letters  
., +, LRA April 2020 1532-1539 SOLAR-GP: Sparse Online Locally Adaptive Regression Using Gaussian Processes for Bayesian Robot Model Learning and Control.  ...  ., +, LRA Oct. 2020 5550-5557 SOLAR-GP: Sparse Online Locally Adaptive Regression Using Gaussian Processes for Bayesian Robot Model Learning and Control.  ... 
doi:10.1109/lra.2020.3032821 fatcat:qrnouccm7jb47ipq6w3erf3cja

Real-time learning of resolved velocity control on a Mitsubishi PA-10

Jan Peters, Duy Nguyen-Tuong
2008 2008 IEEE International Conference on Robotics and Automation  
Learning inverse kinematics has long been fascinating the robot learning community.  ...  While this problem can be treated in various ways in offline learning, it poses a serious problem for online learning.  ...  Thus, the cost function based approach for the creation of a consistent set of local controllers for operational space control can be based on this insight.  ... 
doi:10.1109/robot.2008.4543645 dblp:conf/icra/PetersN08 fatcat:i6oyuzxctvgdhnwtq7ivbyhdym

Table of Contents

2019 IEEE Robotics and Automation Letters  
Lenzi 1186 Vision-Based Online Learning Kinematic Control for Soft Robots Using Local Gaussian Process Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Fang, X.  ...  Kira 1240 IMU-Based Active Safe Control of a Variable Stiffness Soft Actuator M. Silic and K.  ... 
doi:10.1109/lra.2019.2910670 fatcat:tkeo6fc4zrczfjfxivpmrrkajy

Artificial Intelligence in Surgery [article]

Xiao-Yun Zhou, Yao Guo, Mali Shen, Guang-Zhong Yang
2019 arXiv   pre-print
Artificial Intelligence (AI) is gradually changing the practice of surgery with the advanced technological development of imaging, navigation and robotic intervention.  ...  In this article, the recent successful and influential applications of AI in surgery are reviewed from pre-operative planning and intra-operative guidance to the integration of surgical robots.  ...  To this end, learning-based depth estimation, visual odometry and Simultaneous Localization and Mapping (SLAM) have been tailored for camera localization and environment mapping with the use of endoscopic  ... 
arXiv:2001.00627v1 fatcat:dywtv6v36rgf3fummidyluy3zi

Online learning of humanoid robot kinematics under switching tools contexts

Lorenzo Jamone, Bruno Damas, Jose Santos-Victor, Atsuo Takanishi
2013 2013 IEEE International Conference on Robotics and Automation  
This algorithm can directly provide multi-valued regression in a online fashion, while having, for classic single-valued regression, a performance comparable to state-of-the-art online learning algorithms  ...  Using the proposed approach, the robot can dynamically learn how to use different tools, without forgetting the kinematic mappings concerning previously manipulated tools.  ...  Among these techniques, there are a few that were very successful at learning the forward kinematics or inverse dynamics of a robot: Gaussian Processes Regression achieves a state of the art performance  ... 
doi:10.1109/icra.2013.6631263 dblp:conf/icra/JamoneDST13 fatcat:yaiqw5tzhralxntrj2notmzhie

Human-Robot Shared Control for Surgical Robot Based on Context-Aware Sim-to-Real Adaptation [article]

Dandan Zhang, Zicong Wu, Junhong Chen, Ruiqi Zhu, Adnan Munawar, Bo Xiao, Yuan Guan, Hang Su, Wuzhou Hong, Yao Guo, Gregory S. Fischer, Benny Lo (+1 others)
2022 arXiv   pre-print
Learning from demonstration (LfD) techniques can be used to automate some of the surgical subtasks for the construction of the shared control mechanism.  ...  However, a sufficient amount of data is required for the robot to learn the manoeuvres. Using a surgical simulator to collect data is a less resource-demanding approach.  ...  Gaussian Process Regression GPR is a probabilistic supervised machine learning framework that has been proved to be data-efficient and effective for regression [22] .  ... 
arXiv:2204.11116v1 fatcat:vrojjk7auvh5nokk6aykrd5vne

2019 Index IEEE Robotics and Automation Letters Vol. 4

2019 IEEE Robotics and Automation Letters  
., +, LRA July 2019 2691-2698 Vision-Based Online Learning Kinematic Control for Soft Robots Using Local Gaussian Process Regression.  ...  ., +, LRA April 2019 1029-1036 Vision-Based Online Learning Kinematic Control for Soft Robots Using Local Gaussian Process Regression.  ...  Permanent magnets Adaptive Dynamic Control for Magnetically Actuated Medical Robots.  ... 
doi:10.1109/lra.2019.2955867 fatcat:ckastwefh5chhamsravandtnx4

Learning-based Feedback Controller for Deformable Object Manipulation [article]

Biao Jia and Zhe Hu and Zherong Pan and Dinesh Manocha and Jia Pan
2018 arXiv   pre-print
Our online policy learning is based on the Gaussian Process Regression (GPR), which can achieve fast and accurate manipulation and is robust to small perturbations.  ...  The servo-control is accomplished by learning a feedback controller that determines the robotic end-effector's movement according to the deformable object's current status.  ...  For controller parameterization, we first propose a novel nonlinear feedback controller based on Gaussian Process Regression (GPR), which learns the object's deformation behavior online and can accomplish  ... 
arXiv:1806.09618v2 fatcat:nekes2brfjcere3zz5w4j7ttmi

Table of Contents

2021 IEEE Robotics and Automation Letters  
Wang 2147 Kinematics-Based Control of an Inflatable Soft Wearable Robot for Assisting the Shoulder of Industrial Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Tadokoro 3073 FEM-Based Gain-Scheduling Control of a Soft Trunk Robot .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Wu and G.  ... 
doi:10.1109/lra.2021.3072707 fatcat:qyphyzqxfrgg7dxdol4qamrdqu

Table of Contents

2021 IEEE Robotics and Automation Letters  
Aukes 4774 Slip-Based Autonomous ZUPT Through Gaussian Process to Improve Planetary Rover Localization . 4994Path Planning With Automatic Seam Extraction Over Point Cloud Models for Robotic Arc Welding  ...  Li 5642 Structured Prediction for CRiSP Inverse Kinematics Learning With Misspecified Robot Models . , and M.  ... 
doi:10.1109/lra.2021.3095987 fatcat:uyk6vlvv45hifbzj4ruzdi6w54

Dynamical System Modulation for Robot Learning via Kinesthetic Demonstrations

M. Hersch, F. Guenter, S. Calinon, A. Billard
2008 IEEE Transactions on robotics  
It combines a dynamical system control approach with tools of statistical learning theory and provides a solution to the inverse kinematics problem, when dealing with a redundant manipulator.  ...  We present a system for robust robot skill acquisition from kinesthetic demonstrations.  ...  TABLE I SUMMARY I OF GAUSSIAN MIXTURE REGRESSION (GMR).  ... 
doi:10.1109/tro.2008.2006703 fatcat:l5cgjeqttbdffbn6uqq2zg3hpy

Active learning in robotics: A review of control principles

Annalisa T. Taylor, Thomas A. Berrueta, Todd D. Murphey
2021 Mechatronics (Oxford)  
Robots must be able to learn efficiently and flexibly through continuous online deployment.  ...  Active learning is a decision-making process. In both abstract and physical settings, active learning demands both analysis and action.  ...  Acknowledgments We would like to thank Muchen Sun, Ana Pervan, Kyra Rudy, Frank Park, and the anonymous reviewers of the first draft for their many helpful comments on this manuscript.  ... 
doi:10.1016/j.mechatronics.2021.102576 fatcat:qt47bncznzdtdc7ntpmis5dqw4
« Previous Showing results 1 — 15 out of 433 results