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Learning Forward Models for the Operational Space Control of Redundant Robots
[chapter]
2010
Studies in Computational Intelligence
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We present a control approach combining model incremental learning methods with the operational space control approach. ...
We learn the forward kinematics model of a robot and use standard algebraic methods to extract pseudo-inverses and projectors from it. ...
1 -15 Apr 2011
hal-00586453, version 1 -15 Apr 2011 1 Learning Forward Models for the Operational Space Control
Learning Forward Models for the Operational Space Control
The use of a proper ...
doi:10.1007/978-3-642-05181-4_8
fatcat:5zjl7e6zs5cp3jp4jflbo32roq
Control of redundant robots using learned models: An operational space control approach
2009
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems
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Control of redundant robots using learned models: an operational space control approach. ...
Abstract-We present an adaptive control approach combining forward kinematics model learning methods with the operational space control approach. ...
Learning the forward kinematics model with LWPR Learning forward models for a redundant robot does not raise particular problems. ...
doi:10.1109/iros.2009.5354438
dblp:conf/iros/SalaunPS09
fatcat:csieh5ob7fhovh3wfcnfolgxti
Open and closed-loop task space trajectory control of redundant robots using learned models
2013
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
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This paper presents a comparison of open-loop and closed-loop control strategies for tracking a task space trajectory, using redundant robots. ...
We present experiments with a simulated version of the iCub, a highly redundant humanoid robot, in which this learned model is employed to execute both open-loop and closed-loop trajectory control. ...
INTRODUCTION AND RELATED WORK In this work we consider the general problem of trajectory tracking for redundant robots based on learned models: for a given sequence of desired task space points x i d , ...
doi:10.1109/iros.2013.6696348
dblp:conf/iros/DamasJS13
fatcat:64capbbl3rgn3alpaiu3qf34ey
Learning task-space tracking control with kernels
2011
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
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For evaluations, we show in simulation the ability of the method for online model learning for taskspace tracking control of redundant robots. ...
Task-space tracking control is essential for robot manipulation. In practice, task-space control of redundant robot systems is known to be susceptive to modeling errors. ...
INTRODUCTION Control of redundant robots in operational space, especially task-space tracking control, is an essential ability needed in robotics [1] , [2] . ...
doi:10.1109/iros.2011.6094428
dblp:conf/iros/Nguyen-TuongP11
fatcat:mxpae4h7bfbqpdjc5rqgnx32ge
Learning task-space tracking control with kernels
2011
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
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For evaluations, we show in simulation the ability of the method for online model learning for taskspace tracking control of redundant robots. ...
Task-space tracking control is essential for robot manipulation. In practice, task-space control of redundant robot systems is known to be susceptive to modeling errors. ...
INTRODUCTION Control of redundant robots in operational space, especially task-space tracking control, is an essential ability needed in robotics [1] , [2] . ...
doi:10.1109/iros.2011.6048038
fatcat:jfn5sqvgmfbtvill4745yo4voe
Learning Operational Space Control
2006
Robotics: Science and Systems II
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While operational space control is of essential importance for robotics and well-understood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling ...
the operational space controller. ...
CONCLUSION In this paper, a general learning framework for operational space for redundant robots has been presented, which is probably the first successful attempt of learning such control laws to date ...
doi:10.15607/rss.2006.ii.033
dblp:conf/rss/PetersS06
fatcat:dc5uqj5gzfhlfmuxhdjkat2v4q
Learning to Control in Operational Space
2008
The international journal of robotics research
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One of the most general frameworks for phrasing control problems for complex, redundant robots is operational space control. ...
the operational space controller. ...
Discussion & Conclusion In this paper, a general learning framework for operational space control of redundant robots has been presented, which is among the first successful attempts of learning such control ...
doi:10.1177/0278364907087548
fatcat:fpif4lt4pvgjdmry2rtbhxwk5e
Adaptive critic based redundancy resolution scheme for robot manipulators
2009
2009 IEEE International Conference on Systems, Man and Cybernetics
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A novel adaptive critic based kinematic control scheme for a redundant manipulator has been proposed in this paper. ...
The redundancy resolution has been formulated as a discretetime optimal control problem. ...
ACKNOWLEDGMENT This work has been supported by UK-India Education and Research Initiative (UKIERI) grant 'Innovations in Intelligent Assistive Robotics'. ...
doi:10.1109/icsmc.2009.5346160
dblp:conf/smc/BeheraKP09
fatcat:2hoac2g4lvc23jrfdxqkbcdvfy
Online Kernel-Based Learning for Task-Space Tracking Robot Control
2012
IEEE Transactions on Neural Networks and Learning Systems
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For evaluations, we show the ability of the method for online model learning for task-space tracking control of redundant robots. ...
We propose a parametrization for the local model which makes an application in task-space tracking control of redundant robots possible. ...
INTRODUCTION Control of redundant robots in operational space, especially task-space tracking control, is an essential ability needed in robotics [1] , [2] . ...
doi:10.1109/tnnls.2012.2201261
pmid:24807925
fatcat:w5bnhjo57fh67euhegyulystjq
Real-time learning of resolved velocity control on a Mitsubishi PA-10
2008
2008 IEEE International Conference on Robotics and Automation
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Learning inverse kinematics has long been fascinating the robot learning community. ...
Nevertheless, the local solution of the problem depends on the data distribution which can result into inconsistent global solutions with large model discontinuities. ...
We are now in the position to formulate a supervised learning algorithm for the local operational space controllers. ...
doi:10.1109/robot.2008.4543645
dblp:conf/icra/PetersN08
fatcat:i6oyuzxctvgdhnwtq7ivbyhdym
Intrinsically motivated goal exploration for active motor learning in robots: A case study
2010
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems
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The main idea is to push the robot to perform babbling in the goal/operational space, as opposed to motor babbling in the actuator space, by self-generating goals actively and adaptively in regions of ...
We present simulated experiments in a 32 dimensional continuous sensorimotor space showing that 1) exploration in the goal space can be a lot faster than exploration in the actuator space for learning ...
Unlike R-IAC which was made for active learning of forward models, we show that this new algorithm allows for efficient learning of inverse models in redundant robots by leveraging the lower-level dimension ...
doi:10.1109/iros.2010.5651385
dblp:conf/iros/BaranesO10
fatcat:k5wibqj7hbgehmbnx737xhhwti
A Framework for Teaching Impedance Behaviours by Combining Human and Robot 'Best Practice'
2018
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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In this paper, the task critical component is first learned for imitation and a robot-specific controller is then blended into the control using the null space. ...
In doing so, the control cheme takes advantage of both human and robot 'best practice'. ...
The control policy (π) derived so far is in the operational space. In order to reproduce it on robot, it needs to be transformed to the joint space of the robot. ...
doi:10.1109/iros.2018.8593502
dblp:conf/iros/ZhaoSWH18
fatcat:f736s55uznhirph3zf2rffjvrq
Learning local linear Jacobians for flexible and adaptive robot arm control
2011
Genetic Programming and Evolvable Machines
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Successful planning and control of robots strongly depends on the quality of kinematic models, which define mappings between configuration space (e.g. joint angles) and task space (e.g. ...
We focus on learning locally linear forward velocity kinematics models by means of the neuro-evolution approach XCSF. ...
the ISIR lab at UPMC, Paris, for helpful comments and suggestions. ...
doi:10.1007/s10710-011-9147-0
fatcat:5gruwdjiy5bmlfkr2wcdpvfpyy
A Survey for Machine Learning-Based Control of Continuum Robots
2021
Frontiers in Robotics and AI
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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 ...
The redundancy may result from the configuration space, that is, the robot-independent mapping (the configuration space to the task space) is redundant. ...
doi:10.3389/frobt.2021.730330
pmid:34692777
pmcid:PMC8527450
fatcat:p4yeo5jqajfhphzsdbiu746swa
A Forward / Inverse Motor Controller for Cognitive Robotics
[chapter]
2006
Lecture Notes in Computer Science
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A central element of this architecture is a coupled pair of controllers: FMC (forward motor controller: it maps tentative trajectories in the joint space into the corresponding trajectories of the end-effector ...
It operates by alternating two basic operations: 1) relaxation in the configuration space (for reaching a target pose); 2) relaxation in the null space of the kinematic transformation (for producing the ...
feed forward network to learn the kinematic transformation ) (q f x = . ...
doi:10.1007/11840817_63
fatcat:c6c6bix4cfge5cka276flgirny
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