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Model-Based Quality-Diversity Search for Efficient Robot Learning
[article]
2020
arXiv
pre-print
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Our experiments show that enhancing a QD algorithm with such a forward model improves the sample-efficiency and performance of the evolutionary process and the skill adaptation. ...
Furthermore, it is used to adapt the skills of the final repertoire in order to generalize the skills to different scenarios. ...
space -improving the repertoire generalization and skill adaptation efficiency. ...
arXiv:2008.04589v1
fatcat:hqzrezyvjvgxffqobwmshmzxwy
Programming-by-Demonstration and Adaptation of Robot Skills by Fuzzy Time Modeling
2014
International Journal of Humanoid Robotics
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Robot motions are recorded by a data-capturing system and modeled by a specific fuzzy clustering and modeling technique where skill models use time instants as inputs and operator actions as outputs. ...
The updated model is used for further executions of the same skill. ...
While the demonstrator performs a skill, the robot captures the motion data, analyzes it and generates a robot-centered model of the demonstrated robot skill. ...
doi:10.1142/s0219843614500091
fatcat:nr4ae6ojtnhfthsdzgkpwhw2cm
Programming-by-Demonstration and adaptation of robot skills by fuzzy-time-modeling
2011
2011 IEEE Workshop on Robotic Intelligence In Informationally Structured Space
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Robot motions are recorded by a data-capturing system and modeled by a specific fuzzy clustering and modeling technique where skill models use time instants as inputs and operator actions as outputs. ...
The updated model is used for further executions of the same skill. ...
While the demonstrator performs a skill, the robot captures the motion data, analyzes it and generates a robot-centered model of the demonstrated robot skill. ...
doi:10.1109/riiss.2011.5945775
dblp:conf/riiss/PalmI11
fatcat:3ig6ogpkijhpjfjek2rmwqsuya
Interactive Human–Robot Skill Transfer: A Review of Learning Methods and User Experience
2021
Advanced Intelligent Systems
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A broad view of LfD approaches considering user experience. ...
adaptation strategies of the robot where the goal is to learn and teach related motor skills from a few samples. ...
state transition models that generalizes over multiple skills. ...
doi:10.1002/aisy.202000247
fatcat:x7ljmoyi6zhx3a3lwsxiupeh4y
Machine Learning Approaches for Motor Learning: A Short Review
2020
Frontiers in Computer Science
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We identify and describe three types of adaptation: Parameter adaptation in probabilistic models, Transfer and meta-learning in deep neural networks, and Planning adaptation by reinforcement learning. ...
In this short review, we outline existing machine learning models for motor learning and their adaptation capabilities. ...
Meta-learning of movement skills has been proposed in robotics and human-robot interaction to efficiently train robot actions from one or a few demonstrations. ...
doi:10.3389/fcomp.2020.00016
fatcat:rfnjoptaa5bkbayvfcf6eniugq
Machine Learning Approaches For Motor Learning: A Short Review
[article]
2020
arXiv
pre-print
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We identify and describe three types of adaptation: Parameter adaptation in probabilistic models, Transfer and meta-learning in deep neural networks, and Planning adaptation by reinforcement learning. ...
In this short review, we outline existing machine learning models for motor learning and their adaptation capabilities. ...
Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest ...
arXiv:2002.04317v4
fatcat:q6ptmikhh5dd3iaizngkqoaqxq
A Domain-Specific Language for Rich Motor Skill Architectures
[article]
2013
arXiv
pre-print
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The file type is application/pdf.
code-generation for experimentation on technical robot platforms. ...
Our goal is to establish a model-driven development process throughout the project around a domain-specific language (DSL) facilitating the compact description of adaptive modular architectures for rich ...
These approaches are often based on rather generic domain models or ontologies [4] for semantic modeling of robotics systems and translated into DSLs [5] . ...
arXiv:1302.6436v1
fatcat:niis7q7k7rbx7pzfp2cdyl4ab4
Learning and Generalizing Variable Impedance Manipulation Skills from Human Demonstrations
[article]
2021
arXiv
pre-print
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This framework improves robots' adaptability to environment changes(i.e. the weight and shape changes of grasping object at the robot end-effector) and inherits the efficiency of demonstration-variance-based ...
In this paper, we propose a DMP-based framework that learns and generalizes variable impedance manipulation skills from human demonstrations. ...
Learning Variable Impedance Manipulation Skill
robot to generalize the reference stiffness profiles to adapt to
the changes of the robot end-effector. ...
arXiv:2104.01324v3
fatcat:ucczvc7ftzejxlpiih2hf4zxyi
An Improvement of Robot Stiffness-Adaptive Skill Primitive Generalization Using the Surface Electromyography in Human–Robot Collaboration
2021
Frontiers in Neuroscience
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The generalization goals of most skill expression models in real scenarios are specified by humans or associated with other perceptual data. ...
Learning from Demonstration in robotics has proved its efficiency in robot skill learning. ...
AUTHOR CONTRIBUTIONS YG conceptualized the framework, developed the software, designed and conducted the experiments, and wrote the paper. NW and CY supervised, reviewed, and approved the work. ...
doi:10.3389/fnins.2021.694914
pmid:34594181
pmcid:PMC8478287
fatcat:mrggkgfuzjeh7lxls2anzztj3q
A review on manipulation skill acquisition through teleoperation‐based learning from demonstration
2021
Cognitive Computation and Systems
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Manipulation skill learning and generalisation have gained increasing attention due to the wide applications of robot manipulators and the spurt of robot learning techniques. ...
Thus, it is a promising way to transfer manipulation skills from humans to robots by combining the learning methods and teleoperation, and adapting the learned skills to different tasks in new situations ...
Skill representation has a significant impact on the performance of robot skill learning and adaptation. ...
doi:10.1049/ccs2.12005
fatcat:wxyourkvrvcqlh6aht6g5fi3sy
ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing
[article]
2020
arXiv
pre-print
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This is achieved through exploiting the latent representation learned by the underlying Conditional Neural Process (CNP) model, and simultaneous training of the model with supervised learning (SL) for ...
compared to the existing approaches; (iv) ACNMPs can be used to implement skill transfer between robots having different morphology, with competitive learning speeds and importantly with less number of ...
Research Council of Turkey) 2210-A scholarship. ...
arXiv:2003.11334v3
fatcat:szr36j2q3bherpayqlq6fkbzle
ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing
2020
Zenodo
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This is achieved through exploiting the latent representation learned by the underlying Conditional Neural Process (CNP) model, and simultaneous training of the model with supervised learning (SL) for ...
compared to the existing approaches; (iv) ACNMPs can be used to implement skill transfer between robots having different morphology, with competitive learning speeds and importantly with less number of ...
Research Council of Turkey) 2210-A scholarship. ...
doi:10.5281/zenodo.4635920
fatcat:t2gwkpzll5e3lhbr6emnqin5fq
Teaching Human Motion/Force Skills to Robots
1995
Journal of the Robotics Society of Japan
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In this paper we survey a number of methods for teaching human motioni force skills to robots. ...
Essential issues such as representation and identification of human skills as well as consistency of teaching information are addressed. sheng Liu Haruhiko Asada ...
oped perceptionaction models of human skills. ...
doi:10.7210/jrsj.13.592
fatcat:4wnhxrwa2jahnfum2mfv4f72du
Learning Agile Robotic Locomotion Skills by Imitating Animals
[article]
2020
arXiv
pre-print
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Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. ...
expertise of the nuances of each skill. ...
ACKNOWLEDGEMENTS We would like to thank Julian Ibarz, Byron David, Thinh Nguyen, Gus Kouretas, Krista Reymann, Bonny Ho, and the Google Robotics team for their contributions to this work. ...
arXiv:2004.00784v3
fatcat:zxuf7slb3nasxin75p7dqs4ggm
Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing
[article]
2021
arXiv
pre-print
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2018 pre-print arXiv:1810.02422v2 fatcat:g2xtrkex5bhaljgtbzkewkj74aOther Versions
We complete unseen tasks by choosing new sequences of skill latents to control the robot using MPC, where our MPC model is composed of the pre-trained skill policy executed in the simulation environment ...
To address this shortcoming, we present a novel approach to efficiently learning new robotic skills directly on a real robot, based on model-predictive control (MPC) and an algorithm for learning task ...
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the ...
arXiv:1810.02422v3
fatcat:vhvtiimyrnallfodlgeggserei
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