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Learning control method for throwing an object more accurately with one degree of freedom robot

Hideyuki Miyashita, Tasuku Yamawaki, Masahito Yashima
2010 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics  
Instead of raising the accuracy of the throwing model itself, we overcome the problems of the approximate model with errors by proposing the position-based iterative learning control method.  ...  Due to a model uncertainty, it is generally difficult to obtain the exact model and find appropriate control inputs to realize the throwing manipulation by experiments.  ...  ITERATIVE LEARNING CONTROL A.  ... 
doi:10.1109/aim.2010.5695807 fatcat:k2wbjapewjcmli3al2pm2lga3i

Parts Assembly and Sorting by Throwing Manipulation: Planning and Control

Hideyuki MIYASHITA, Tasuku YAMAWAKI, Masahito YASHIMA
2011 Journal of System Design and Dynamics  
This paper presents optimal trajectory planning and iterative learning control for a throwing manipulation which can control not only the position but also the orientation of a polygonal object robustly  ...  We also demonstrate the usefulness of the throwing manipulation by applying it to the parts assembly and sorting on experiments.  ...  (17) proposed a control method to throw the object to any position in the vertical plane by integrating the learning control method and optimization techniques.  ... 
doi:10.1299/jsdd.5.139 fatcat:zd2e3i722ngkxl5pme7c2ezgha

Policies for Goal Directed Multi-Finger Manipulation [article]

Sheldon Andrews, Paul G. Kry
2012 Workshop on Virtual Reality Interactions and Physical Simulations  
Our approach uses a mid-level multiphase approach to break the problem into three parts, providing an appropriate control strategy for each phase and resulting in cyclic finger motions that accomplish  ...  The offline simulations used to learn the policy are effective solutions for the task, but an important aspect of our work is that the policy is general enough to be used online in real time.  ...  This work was supported by funding from NSERC and GRAND NCE.  ... 
doi:10.2312/pe/vriphys/vriphys12/137-145 dblp:conf/vriphys/AndrewsK12 fatcat:jay3wokrffc65i57e2ap6ssx7e

Parts assembly by throwing manipulation with a one-joint arm

H Miyashita, T Yamawaki, M Yashima
2010 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems  
The present paper proposes the learning control method for the throwing manipulation which can control not only the position but also the orientation of the polygonal object more accurately and robustly  ...  We also demonstrate the usefulness of the throwing manipulation by applying it to sorting task and assembly task on experiments.  ...  These videos show the throwing manipulation controlled by the proposed learning algorithm can achieve the high positioning performance. VII.  ... 
doi:10.1109/iros.2010.5650879 dblp:conf/iros/MiyashitaYY10 fatcat:gfmjyktskzfyxa3jag2tetphlu

An adaptive switching learning control method for trajectory tracking of robot manipulators

P.R. Ouyang, W.J. Zhang, Madan M. Gupta
2006 Mechatronics (Oxford)  
In this paper, a new adaptive switching learning control approach, called adaptive switching learning PD control (ASL-PD), is proposed for trajectory tracking of robot manipulators in an iterative operation  ...  The convergence rate with the ASL-PD method is faster than that of the adaptive iterative learning control method proposed by others in literature.  ...  Acknowledgement This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) through a PGS-B scholarship to the first author and partially supported by NSERC through  ... 
doi:10.1016/j.mechatronics.2005.08.002 fatcat:tjerqsrcd5fufgegcgceo26kw4

Goal directed multi-finger manipulation: Control policies and analysis

S. Andrews, P.G. Kry
2013 Computers & graphics  
All motion is physically based and guided by a control policy that is learned through a series of offline simulations. We also discuss practical considerations for our learning method.  ...  The exact trajectory of the object is never specified since the goal is defined by the final orientation and position of the object.  ...  This work was supported by funding from NSERC and GRAND NCE.  ... 
doi:10.1016/j.cag.2013.04.007 fatcat:eyhl6ij5gnet5ihkfikgqvhbna

Iterative Learning Control for Positioning in Releasing Manipulation
リリース型マニピュレーションにおける位置決め用の繰り返し学習制御

Chi Zhu, Yasumichi Aiyama, Tamio Arai, Atsuo Kawamura, Tetsumi Harakawa
2009 Journal of the Robotics Society of Japan  
Aiming to improve positioning precision of stop posture (position and orientation) of an object and decrease trial numbers in our proposed releasing manipulation, two iterative learning control (ILC) schemes  ...  , learning control based on convergent condition (LCBCC), and learning control based on optimal principle (LCBOP) are designed in an experimental-oriented way.  ...  Learning Control [1] [2] [4] [6] [7] 3 1 2 LCBCC Learn- ing Control Based on Convergent Condition LCBOP Learning Control Based on Optimal Principle 3 1. 2 [8] [9] [10] Task-Level  ... 
doi:10.7210/jrsj.27.169 fatcat:ei636oyjkvhylkjnos2pcyfdq4

Sampling-based motion planning with dynamic intermediate state objectives: Application to throwing

Yajia Zhang, Jingru Luo, Kris Hauser
2012 2012 IEEE International Conference on Robotics and Automation  
Dynamic manipulations require attaining high velocities at specified configurations, all the while obeying geometric and dynamic constraints.  ...  This paper presents a motion planner that constructs a trajectory that passes at an intermediate state through a dynamic objective region, which is comprised of a certain lower dimensional submanifold in  ...  Using several practice iterations, the learning procedures adapt the task model and control policy to compensate for systematic inaccuracies.  ... 
doi:10.1109/icra.2012.6225319 dblp:conf/icra/ZhangLH12 fatcat:s6rlvvsccjgofkcwb3pjxb3vee

Robotics in Biomedical and Healthcare Engineering

Chengzhi Hu, Qing Shi, Lianqing Liu, Uche Wejinya, Yasuhisa Hasegawa, Yajing Shen
2017 Journal of Healthcare Engineering  
Patient's training trajectory can be corrected by integrating the iterative learning control scheme with the value of impedance.  ...  Guo and coworkers proposed an impedance-based iterative learning control method to analyze the squatting training of stroke patients in the iterative domain and time domain.  ...  Patient's training trajectory can be corrected by integrating the iterative learning control scheme with the value of impedance.  ... 
doi:10.1155/2017/1610372 pmid:29065573 pmcid:PMC5592411 fatcat:cigeqt5mbjbl5gcmm4pmwahqw4

Multi-modal data-driven motion planning and synthesis

Mentar Mahmudi, Marcelo Kallmann
2015 Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games - SA '15  
Our method is able to automatically generate complex motions with precise manipulation targets among obstacles and in coordination with locomotion.  ...  Our approach decouples the problem in specialized locomotion and manipulation skills, and proposes a multi-modal planning scheme that explores the search space of each skill together with the possible  ...  Although reinforcement learning is a promising direction to generate controllable motion, it is time-consuming to learn control policies and controllers have to remain in a low dimensional space.  ... 
doi:10.1145/2822013.2822044 dblp:conf/mig/MahmudiK15 fatcat:2amddawj7na47anxjs6cmsueuu

Neuro-Fuzzy based Approach for Inverse Kinematics Solution of Industrial Robot Manipulators

Srinivasan Alavandar, M. J. Nigam
2008 International Journal of Computers Communications & Control  
Obtaining the joint variables that result in a desired position of the robot end-effector called as inverse kinematics is one of the most important problems in robot kinematics and control.  ...  In this paper, using the ability of ANFIS (Adaptive Neuro-Fuzzy Inference System) to learn from training data, it is possible to create ANFIS, an implementation of a representative fuzzy inference system  ...  The authors wish to thank the program committee of ICCCC 2008 for the recommendation of an extended version for publication in the Journal. Bibliography  ... 
doi:10.15837/ijccc.2008.3.2391 fatcat:qeccnx2invhy3i7kddi2ddmv4y

A universal control architecture for maritime cranes and robots using genetic algorithms as a possible mapping approach

F. Sanfilippo, L. I. Hatledal, H. G. Schaathun, K. Y. Pettersen, H. Zhang
2013 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)  
The manipulators that are to be controlled can be added to the system simply by defining the corresponding Denavit-Hartenberg table and their joint limits.  ...  This paper introduces a flexible and general control system architecture that allows for modelling, simulation and control of different models of maritime cranes and, more generally, robotic arms by using  ...  mode, or by: x t = x a +ẋ ds ∆t, (7) if operating in velocity control mode, where ∆t is the time interval between two successive iterations. 2) Acquire manipulator model, control mode and target position  ... 
doi:10.1109/robio.2013.6739479 dblp:conf/robio/SanfilippoHSPZ13 fatcat:qbz4jlw2grf53dtg7mlsjgljhu

Cooperative suspended object manipulation using reinforcement learning and energy-based control

Ivana Palunko, Philine Donner, Martin Buss, Sandra Hirche
2014 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems  
In order to be able to inject energy into various suspended objects of unknown parameters, in this paper we propose an adaptive controller which combines reinforcement learning with energy based swing-up  ...  Cooperative dynamic object manipulation can extend the manipulation capabilities of robot-robot and humanrobot teams.  ...  Around 345 s the reference is set to zero, which means we want the controller to release the energy which is reflected in the negative a.  ... 
doi:10.1109/iros.2014.6942664 dblp:conf/iros/PalunkoDBH14 fatcat:zotnkvmpsjgqrpr7nyiypwllf4

Iterative learning control for flexible manipulator using fourier basis function

Li Zhang, Shan Liu
2015 International Journal of Automation and Computing  
Perfect tracking of the tip position of a flexible-link manipulator (FLM) is unable to be achieved by causal control because it is a typical non-minimum phase system.  ...  In this method, an iterative identification algorithm is used to construct the Fourier basis function space model of the manipulator, and a pseudoinverse type iterative learning law is designed to approximate  ...  Iterative learning control [12] (ILC) can be used for implementing non-causal control because it is able to predict future information of the controlled system by using system input and output of previous  ... 
doi:10.1007/s11633-015-0932-8 fatcat:skinrq5wtzbrnaoam5sjtdzn4q

A Versatile Data-Driven Framework for Model-Independent Control of Continuum Manipulators Interacting With Obstructed Environments With Unknown Geometry and Stiffness [article]

Farshid Alambeigi, Zerui Wang, Yun-Hui Liu, Russell H. Taylor, Mehran Armand
2020 arXiv   pre-print
This optimal iterative algorithm learns the deformation behavior of the CM in interaction with an unknown environment, in real time, and then accomplishes the defined control objective.  ...  The performance and learning capability of the framework was investigated in 11 sets of experiments including PMI position and shape control in free and unknown obstructed environments as well as during  ...  control proposed by [4] .  ... 
arXiv:2005.01951v1 fatcat:c4xz3c6befhzjkybclf2sizwju
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