Letters: Adaptive biomimetic control of robot arm motions
TL;DR: A biologically inspired robotic model that combines a modified feedback error learning, an unsupervised learning, and the viscoelastic actuator system in order to drive adaptive arm motions demonstrates the potential usefulness of a biomimetic design of robot skill.
About: This article is published in Neurocomputing.The article was published on 2008-10-01. It has received 8 citations till now. The article focuses on the topics: Robot control & Robot learning.
Citations
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01 Jul 2009
TL;DR: In this article, a two-link planar mechanical manipulator that emulates a human arm is modeled and controlled using an active force control strategy to compensate for the vibration effect.
Abstract: This article focuses on the modelling and control of a two-link planar mechanical manipulator that emulates a human arm. The simplicity of the control algorithm and its ease of computation are particularly highlighted in this study. The arm is subjected to a vibratory excitation at a specific location on the arm while performing trajectory tracking tasks in two-dimensional space, taking into account the presence of 'muscle' elements that are mathematically modelled. A closed-loop control system is applied using an active force control strategy to accommodate the disturbances based on a predefined set of loading and operating conditions to observe the system responses. Results of the study imply the effectiveness of the proposed method in compensating the vibration effect to produce robust and accurate tracking performance of the system. The results may serve as a useful tool in aiding the design and development of a tooling device for use in a mechatronic robot arm or even human arm (smart glove) where precise and/or robust performance is a critical factor and of considerable importance.
19 citations
Dissertation•
01 Jul 2012
7 citations
Cites background from "Letters: Adaptive biomimetic contro..."
...Design and control 2008 Feedback error and unsupervised learning based control system for robotic adaptive arm motions, inspired by the learning from the cerebellum and the elasticity of the muscles (Sungho, 2008)....
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TL;DR: A Particle Swarm Optimization technique is anticipated to suggest that reduce the computation time as well as make the output result as much closer to the true value (i.e.,) experimentally obtained value.
Abstract: In this modern world, robotic evaluation plays a most important role. In secure distance, this leads the humans to execute insecure task. To acquire an effective result, the system which makes the human task easier should be taken care of and the holdup behind the system should be eradicated. Only static parameters are considered and such parameters are not enough to obtain optimized value in existing work. For consecutively attaining optimized value in our previous work, we focused on both static and dynamic parameters in the robotic arm gearbox model. Now, a genetic algorithm is utilized and the result obtained is greater than the existing work. On the other hand, to attain an effective result the genetic algorithm itself is not enough since it takes massive time for computation process and the result obtained in this computation is not as much closer to the true value. By eliminating all those aforementioned issues, a proper algorithm needs to be utilized in order to achieve an efficient result than the existing and our previous works. In this paper, we anticipated to suggest a Particle Swarm Optimization technique that reduce the computation time as well as make the output result as much closer to the true value (i.e.,) experimentally obtained value.
5 citations
01 Dec 2015
3 citations
Cites background from "Letters: Adaptive biomimetic contro..."
...During the last decade, the robot arms are getting more and more closer to the human multi-joint arms by measuring or capturing the human multi-joint arm motion and converting to motion of the robot arm [35, 36]....
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TL;DR: A newly developed learning strategy (‘learning by averaging’) shows consistent success if the motor task is constrained by special requirements and indicates a general superiority of DIM if combined with abstract recurrent neural networks.
Abstract: This paper focuses on adaptive motor control in the kinematic domain. Several motor-learning strategies from the literature are adopted to kinematic problems: ‘feedback-error learning’, ‘distal supervised learning’, and ‘direct inverse modelling’ (DIM). One of these learning strategies, DIM, is significantly enhanced by combining it with abstract recurrent neural networks. Moreover, a newly developed learning strategy (‘learning by averaging’) is presented in detail. The performance of these learning strategies is compared with different learning tasks on two simulated robot setups (a robot-camera-head and a planar arm). The results indicate a general superiority of DIM if combined with abstract recurrent neural networks. Learning by averaging shows consistent success if the motor task is constrained by special requirements.
2 citations
Cites background from "Letters: Adaptive biomimetic contro..."
...…learning FEL has been developed in the context of dynamic motor control (Kawato, Furukawa, and Suzuki 1987; Kawato 1990; Gomi and Kawato 1993; Nakanishi and Schaal 2004; Jo 2008; Kambara, Kim, Shin, Sato, and Koike 2009), but it can also be adopted in modified form to kinematic problems....
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References
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Book•
01 Jan 1991
TL;DR: Covers in a progressive fashion a number of analysis tools and design techniques directly applicable to nonlinear control problems in high performance systems (in aerospace, robotics and automotive areas).
Abstract: Covers in a progressive fashion a number of analysis tools and design techniques directly applicable to nonlinear control problems in high performance systems (in aerospace, robotics and automotive areas).
15,545 citations
Book•
06 Oct 2003
TL;DR: A fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
Abstract: Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
8,091 citations
"Letters: Adaptive biomimetic contro..." refers result in this paper
...In another viewpoint, the learning scheme is equivalent to the Hebbian rule [10]....
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Book•
06 Oct 2003
TL;DR: In this paper, the mathematics underpinning the most dynamic areas of modern science and engineering are discussed and discussed in a fun and exciting textbook on the mathematics underlying the most important areas of science and technology.
Abstract: Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
7,243 citations
TL;DR: A modular approach to motor learning and control based on multiple pairs of inverse (controller) and forward (predictor) models that can simultaneously learn the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given environment is proposed.
2,101 citations
"Letters: Adaptive biomimetic contro..." refers background in this paper
...FEL describes the adaptive feedback control as a computational model of the functional role of the cerebellum [5,9,18]....
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TL;DR: It is shown that combinations of three time-varying muscle synergies underlie the variety of muscle patterns required to kick in different directions, that the recruitment of these synergies is related to movement kinematics, and that there are similarities among the synergies extracted from different behaviors.
Abstract: A central issue in motor control is how the central nervous system generates the muscle activity patterns necessary to achieve a variety of behavioral goals. The many degrees of freedom of the musculoskeletal apparatus provide great flexibility but make the control problem extremely complex. Muscle synergies—coherent activations, in space or time, of a group of muscles—have been proposed as building blocks that could simplify the construction of motor behaviors. To evaluate this hypothesis, we developed a new method to extract invariant spatiotemporal components from the simultaneous recordings of the activity of many muscles. We used this technique to analyze the muscle patterns of intact and unrestrained frogs during kicking, a natural defensive behavior. Here we show that combinations of three time-varying muscle synergies underlie the variety of muscle patterns required to kick in different directions, that the recruitment of these synergies is related to movement kinematics, and that there are similarities among the synergies extracted from different behaviors.
1,158 citations
"Letters: Adaptive biomimetic contro..." refers background or methods in this paper
...Experimentally it was observed that, using a frog hind limb, synergy underlies a variety of muscular activations that produce different behaviors, and proposed that the synergy is coded within the spinal cord [3,17] and furthermore the spinal motor system participates actively in the behavioral adaptation [2]....
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...The muscle synergy terms a muscle group specified by a principal waveform [2,3,17]....
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