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Learning an internal representation of the end-effector configuration space
[article]
2018
arXiv
pre-print
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An internal representation of the end-effector configuration is generated from unstructured proprioceptive and exteroceptive data flow under very limited assumptions. ...
A mapping from the proprioceptive space to this representational space can then be used to control the robot. ...
The goal of the robot is to create an internal representation ξ of the endeffector configuration using topological information of the exteroceptive space. space of end-effector configurations C. ...
arXiv:1810.01866v1
fatcat:xwklqtgqs5ekjh2vacnz57nrjy
A Self-Organizing Neural Model of Motor Equivalent Reaching and Tool Use by a Multijoint Arm
1993
Journal of Cognitive Neuroscience
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The Need for Internal Spatial Representations Several different phenomena fall under the general heading of motor equivalence. ...
Central to the model is an analysis of how visual, spatial, and motor representations are formed and combined for the control of goal-oriented reaching. ...
As noted above, both internal and external feedback loops exist for updating the internal representation of end effector position. ...
doi:10.1162/jocn.1993.5.4.408
pmid:23964916
fatcat:45giaas6jbf6np3oqsf4jhmuxi
Population based Mean of Multiple Computations networks: A building block for kinematic models
2015
2015 International Joint Conference on Neural Networks (IJCNN)
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Such a representation will be introduced and applied for the case of a redundant manipulator. ...
The local transformations in between the kinematic variables can be sufficiently well learned by small single MLP layers. ...
This is achieved by shaping the models attractor space to represent valid combinations of joint-configurations and end-effector positions. ...
doi:10.1109/ijcnn.2015.7280791
dblp:conf/ijcnn/BaumMS15
fatcat:vuf5waidyvc35bgotb6b2fdqke
Learning local linear Jacobians for flexible and adaptive robot arm control
2011
Genetic Programming and Evolvable Machines
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We show that XCSF can learn large forward velocity kinematic mappings autonomously and rather independently of the task space representation provided. ...
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. ...
Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided ...
doi:10.1007/s10710-011-9147-0
fatcat:5gruwdjiy5bmlfkr2wcdpvfpyy
Representation and generalization of bi-manual skills from kinesthetic teaching
2012
2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)
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Skills are learned and embedded over several representational levels comprising a compact movement representation by means of movement primitives, a task space description of the bi-manual tool constraint ...
By means of comparative evaluation of different architectural configurations, a specific modulation of skill execution with respect to the embedding in the robot's workspace is identified to achieve optimal ...
An automated data analysis is conducted to determine the task space coordinates g of the guiding hand, i.e. the end effector trajectory with the highest spatial variance. ...
doi:10.1109/humanoids.2012.6651575
dblp:conf/humanoids/ReinhartLS12
fatcat:7l43q4mga5hmlc6wm7rv74ttfq
Transfer of knowledge for a climbing Virtual Human: A reinforcement learning approach
2009
2009 IEEE International Conference on Robotics and Automation
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In this paper we present a transfer framework adapted to the case of a climbing Virtual Human (VH). We show that our VH learns faster to climb a wall after having learnt on a different previous wall. ...
In the reinforcement learning literature, transfer is the capability to reuse on a new problem what has been learnt from previous experiences on similar problems. ...
The authors would like to thank Cyrille Collette (CEA/LIST) for his help in the use of the VH and ENSTA ParisTech for granting access to their machines. ...
doi:10.1109/robot.2009.5152553
dblp:conf/icra/LibeauMS09
fatcat:d6wevn3sa5fy7g3fqsccm5bd2u
Building a Sensorimotor Representation of a Naive Agent's Tactile Space
2017
IEEE Transactions on Cognitive and Developmental Systems
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The presented work is based in this idea, and a method for the building of an internal representation of its sensorimotor interaction is proposed. ...
Moreover, plausibility of the internal sensorimotor representation is highlighted by showing that it is possible to consider motion planning directly from it. ...
an internal representation of the interaction between an agent with its own body. ...
doi:10.1109/tcds.2016.2617922
fatcat:kiexyjclofffjpouva2bz3udoy
Learning Global Inverse Statics Solution for a Redundant Soft Robot
2016
Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics
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The results indicate that learning based approaches could be an effective method for modelling and control of complex soft robots, especially for high dimensional redundant robots. ...
A unique inverse statics formulation and data sampling method enables the learning system to circumvent the main roadblocks of the inverting problem. ...
of Foreign Affairs and International Cooperation DGSP-UST for the support through Joint Laboratory on Biorobotics Engineering project. ...
doi:10.5220/0005979403030310
dblp:conf/icinco/ThuruthelFCRL16
fatcat:hyk25du2y5bhlbmo5wrzui6hxa
Training a spiking neural network to control a 4-DoF robotic arm based on Spike Timing-Dependent Plasticity
2010
The 2010 International Joint Conference on Neural Networks (IJCNN)
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Its aim is to provide the joint commands that will move the end-effector in a desired spatial direction, given the joint configuration of the arm. ...
The architecture is a feed-forward network where the input layers encode the intended movement direction of the end-effector in spatial coordinates, as well as the information that is given by proprioception ...
ACKNOWLEDGMENT This work was supported by an EPSRC Grant under Project Code EP/F033516/1. ...
doi:10.1109/ijcnn.2010.5596525
dblp:conf/ijcnn/BouganisS10
fatcat:egbzagura5d6jg6g7ylpq5g3vm
A neural controller for collision-free movement of general robot manipulators
1988
IEEE International Conference on Neural Networks
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program employing these equations is written to generate arm c o n Q " sequences that move the robot's end-effector from its current position to a target position. ...
In this paper we show how the mhitectum is able to learn to gsnerate, in constant time, many altanative solution configurations which touch a target point, and we show how leamed collision constraints ...
During a preliminary training phase, it must learn to associate eye angle vectors with one or more target arm configurations that place the end-effector within an allowable distance from the target point ...
doi:10.1109/icnn.1988.23831
dblp:conf/icnn/GrafL88
fatcat:t5nb2htw4bd4fecthwubg3dm4y
Topology-based representations for motion planning and generalization in dynamic environments with interactions
2013
The international journal of robotics research
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Motion can be described in several alternative representations, including joint configuration or end-effector spaces, but also more complex topology-based representations that imply a change of Voronoi ...
This allows for consistent combination of multiple representations (e.g. across task, end-effector and joint space). ...
This is in strong contrast to thinking of y * 0:T as a lower-dimensional task space like an end-effector space. ...
doi:10.1177/0278364913482017
fatcat:pme4hlj5tfbkxk4as4cpkyyciy
The neural dynamics of goal-directed arm movements: A developmental perspective
2015
2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)
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generation of the time courses of virtual trajectories of the hand in Cartesian space, and their transformation into virtual joint trajectories and muscle forces. ...
to joint space, are decalibrated to reflect earlier stages of development. ...
This work reflects only the authors' views; the EC is not liable for any use that may be made of the information contained herein. ...
doi:10.1109/devlrn.2015.7346134
dblp:conf/icdl-epirob/ZibnerTS15a
fatcat:m2ntqat5lng4jkyd5glrzzmgly
Visually guided movements: learning with modular neural maps in robotics
1998
Neural Networks
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Therefore, we address this problem starting with the architecture of the system. We illustrate this approach using a robotic application: the visual servoing of the arm's end-effector. ...
This paper proposes the idea that biological learning can take advantage of the structures of the modules and the nature of modular decomposition. ...
An output connected to the input of another module constitutes an internal information channel. The data of this channel are named internal representation. ...
doi:10.1016/s0893-6080(98)00050-1
pmid:12662757
fatcat:kgo65xjh2fdypks3slbmck7cky
Efficient learning of constraints and generic null space policies
2017
2017 IEEE International Conference on Robotics and Automation (ICRA)
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A large class of motions can be decomposed into a movement task and null-space policy subject to a set of constraints. ...
This novel formulation of the problem allows the constraint learning method to be coupled with the policy learning method to improve policy learning accuracy, which enables us to learn more complex motions ...
of the end-effector, and p ee is a vector that represents the position of the end-effector. ...
doi:10.1109/icra.2017.7989181
dblp:conf/icra/ArmestoBIV17
fatcat:w47plxmhbzevzgqu6bx275wowe
Adaptive nonparametric kinematic modeling of concentric tube robots
2016
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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We evaluated our approach on data gathered from a three-tube robot, and report high accuracy across the robot's configuration space. ...
The model comprises an ensemble of linear models, each of which locally approximates the original complex kinematic relation. ...
Hence, for an n-tube robot, a point in the configuration space is defined as: x = [α i1 , d i1 , ..., α n1 , d n1 ] T ∈ R 2n−2 . The task space comprises all feasible end-effector (tip) poses. ...
doi:10.1109/iros.2016.7759636
pmid:28717555
pmcid:PMC5510657
dblp:conf/iros/FagogenisBD16
fatcat:m5kbgltspfgzjhm6cnx5of6k4i
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