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نتیجه جستجو - Imitation learning

تعداد مقالات یافته شده: 6
ردیف عنوان نوع
1 Reinforcement learning based on movement primitives for contact tasks
یادگیری تقویتی بر اساس ابتدای حرکت برای وظایف تماس-2020
Recently, robot learning through deep reinforcement learning has incorporated various robot tasks through deep neural networks, without using specific control or recognition algorithms. However, this learning method is difficult to apply to the contact tasks of a robot, due to the exertion of excessive force from the random search process of reinforcement learning. Therefore, when applying reinforcement learning to contact tasks, solving the contact problem using an existing force controller is necessary. A neural-network-based movement primitive (NNMP) that generates a continuous trajectory which can be transmitted to the force controller and learned through a deep deterministic policy gradient (DDPG) algorithm is proposed for this study. In addition, an imitation learning algorithm suitable for NNMP is proposed such that the trajectories similar to the demonstration trajectory are stably generated. The performance of the proposed algorithms was verified using a square peg-in-hole assembly task with a tolerance of 0.1 mm. The results confirm that the complicated assembly trajectory can be learned stably through NNMP by the proposed imitation learning algorithm, and that the assembly trajectory is improved by learning the proposed NNMP through the DDPG algorithm.
Keywords: AI-based methods | Force control | Deep Learning in robotics and automation
مقاله انگلیسی
2 From inverse optimal control to inverse reinforcement learning: A historical review
از کنترل بهینه معکوس تا یادگیری تقویتی معکوس: یک بررسی تاریخی-2020
Inverse optimal control (IOC) is a powerful theory that addresses the inverse problems in control systems, robotics, Machine Learning (ML) and optimization taking into account the optimal manners. This paper reviews the history of the IOC and Inverse Reinforcement Learning (IRL) approaches and describes the connections and differences between them to cover the research gap in the existing literature. The gen- eral formulation of IOC/IRL is described and the related methods are categorized based on a hierarchical approach. For this purpose, IOC methods are categorized under two classes, namely classic and modern approaches. The classic IOC is typically formulated for control systems, while IRL, as a modern approach to IOC, is considered for machine learning problems. Despite the presence of a handful of IOC/IRL meth- ods, a comprehensive categorization of these methods is lacking. In addition to the IOC/IRL problems, this paper elaborates, where necessary, on other relevant concepts such as Learning from Demonstration (LfD), Imitation Learning (IL), and Behavioral Cloning. Some of the challenges encountered in the IOC/IRL problems are further discussed in this work, including ill-posedness, non-convexity, data availability, non- linearity, the curses of complexity and dimensionality, feature selection, and generalizability.
Keywords: Inverse optimal control | Inverse reinforcement learning | Learning from demonstration | Imitation learning
مقاله انگلیسی
3 An end-to-end inverse reinforcement learning by a boosting approach with relative entropy
یک یادگیری تقویت معکوس پایان به پایان با یک رویکرد تقویتی با آنتروپی نسبی-2020
Inverse reinforcement learning (IRL) involves imitating expert behaviors by recovering re- ward functions from demonstrations. This study proposes a model-free IRL algorithm to solve the dilemma of predicting the unknown reward function. The proposed end-to-end model comprises a dual structure of autoencoders in parallel. The model uses a state encoding method to reduce the computational complexity for high-dimensional environ- ments and utilizes an Adaboost classifier to determine the difference between the pre- dicted and demonstrated reward functions. Relative entropy is used as a metric to measure the difference between the demonstrated and the imitated behavior. The simulation exper- iments demonstrate the effectiveness of the proposed method in terms of the number of iterations that are required for the estimation.
Keywords: Inverse reinforcement learning | Imitation learning | State encoding | Adaboost | Relative entropy
مقاله انگلیسی
4 Active deep learning for the identification of concepts and relations in electroencephalography reports
یادگیری عمیق فعال برای شناسایی مفاهیم و روابط در گزارشات الکتروانسفالوگرافی-2019
The identification of medical concepts, their attributes and the relations between concepts in a large corpus of Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort retrieval system. However, the recognition of multiple types of medical concepts, along with the many attributes characterizing them is challenging, and so is the recognition of the possible relations between them, especially when desiring to make use of active learning. To address these challenges, in this paper we present the Self- Attention Concept, Attribute and Relation (SACAR) identifier, which relies on a powerful encoding mechanism based on the recently introduced Transformer neural architecture (Dehghani et al., 2018). The SACAR identifier enabled us to consider a recently introduced framework for active learning which uses deep imitation learning for its selection policy. Our experimental results show that SACAR was able to identify medical concepts more precisely and exhibited enhanced recall, compared with previous methods. Moreover, SACAR achieves superior performance in attribute classification for attribute categories of interest, while identifying the relations between concepts with performance competitive with our previous techniques. As a multi-task network, SACAR achieves this performance on the three prediction tasks simultaneously, with a single, complex neural network. The learning curves obtained in the active learning process when using the novel Active Learning Policy Neural Network (ALPNN) show a significant increase in performance as the active learning progresses. These promising results enable the extraction of clinical knowledge available in a large collection of EEG reports.
Keywords: Deep learning | Electroencephalography | Active learning | Long-distance relation identification | Concept detection | Attribute classification
مقاله انگلیسی
5 Generation of rhythmic hand movements in humanoid robots by a neural imitation learning architecture
تولید حرکات دست ریتمیک در ربات انسان نما بواسطه ی معماری عصبی آموزش تقلید-2017
This paper presents a two layer system for imitation learning in humanoid robots. The first layer of this system records complicated and rhythmic movement of the trainer using a motion capture device. It solves an inverse kinematic problem with the help of an adaptive Neuro-Fuzzy Inference system. Then it can achieve angles records of any joints involved in the desired motion. The trajectory is given as input to the systems second layer. The layer deals with extracting optimal parameters of the trajectories obtained from the first layer using a network of oscillator neurons and Particle Swarm Optimization algo- rithm. This system is capable to obtain any complex motion and rhythmic trajectory via first layer and learns rhythmic trajectories in the second layer then converge towards all these movements. Moreover, this two layer system is able to provide various features of a learner model, for instance resis- tance against perturbations, modulation of trajectories amplitude and frequency. The simulation results of the learning system is performed in the robot simulator WEBOTS linked with MATLAB software. Practical implementation on an NAO robot demonstrate that the robot has learned desired motion with high accuracy. These results show that proposed system in this paper produces high convergence rate and low test error.© 2016 Published by Elsevier B.V.
Keywords:Imitation learning | Neural networks | Central pattern generator
مقاله انگلیسی
6 Demonstration learning of robotic skills using repeated suggestions learning algorithm
یادگیری مهارت های تظاهرات رباتیک با استفاده از پیشنهادات مکرر الگوریتم یادگیری-2017
In this paper a new model of nonlinear dynamical system based on adaptive frequency oscillators for learning rhythmic signals is implemented by demonstration. This model uses coupled Hopf oscillators to encode and learn any periodic input signal. Learning process is completely implemented in the dynam- ics of adaptive oscillators. One of the issues in learning in such systems is constant number of oscillators in the feedback loop. In other words, the number of adaptive frequency oscillators is one of the design factors. In this contribution, it is shown that using enough number of oscillators can help the learning process. In this paper, we address this challenge and try to solve it in order to learn the rhythmic move- ments with greater accuracy, lower error and avoid missing fundamental frequency. To reach this aim, a method for generating drumming patterns is proposed which is able to generate rhythmic and periodic trajectories for a NAO humanoid robot. To do so, a programmable central pattern generator is used which is inspired from animal’s neural systems and these programmable central pattern generators are extended to learn patterns with more accuracy for NAO humanoid robots. Successful experiments of demonstration learning are done using simulation and a NAO Real robot.© 2017 Elsevier B.V. All rights reserved.
Keywords:Imitation learning | Hopf oscillator | Adaptive frequency oscillator | Central pattern generator | Humanoid robots
مقاله انگلیسی
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