Inverse discounted-based LQR algorithm for learning human movement behaviors

El-Hussieny, Haitham; Ryu, Jee-Hwan

doi:10.1007/s10489-018-1331-y

Cited by 17 publications

(10 citation statements)

References 33 publications

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“…The IOC/IRL [82][83][84] problems seek the weights of the utility function n x; u ð Þ that are responsible for obtaining an optimal or expert decision making function. These methods are considered as a striatum cognitive models because they take cognitive models of both the neocortex and hippocampus for complementary learning and experience transference.…”

Section: Inverse Optimal Control/inverse Reinforcement Learningmentioning

confidence: 99%

“…However, there exists multiple solutions that derive the same performance and we require to add some constraints. The most common approaches to solve optimization problems under constraints use a convex optimization algorithm [84] based on linear or quadratic programming methods [83,50] to obtain the optimal variable or variables of interest.…”

Section: Optimization Techniquesmentioning

confidence: 99%

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Human-behavior learning: A new complementary learning perspective for optimal decision making controllers

Perrusquía

2022

Neurocomputing

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Section: Inverse Optimal Control/inverse Reinforcement Learningmentioning

confidence: 99%

Section: Optimization Techniquesmentioning

confidence: 99%

Human-behavior learning: A new complementary learning perspective for optimal decision making controllers

Perrusquía

2022

Neurocomputing

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“…with f : R + × R n × R p h → R n and where x ∈ R n and u (h) ∈ R p h are the system states and the inputs of the human, respectively. A quadratic cost function for modelling human actions (see [23], [24])…”

Section: A Modelling Shared Controllers By Differential Gamesmentioning

confidence: 99%

Limited Information Shared Control: A Potential Game Approach

Varga,

Inga,

Hohmann

2022

Preprint

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This paper presents a systematic method for the design of a limited information shared controller (LISC). LISC is used in applications where not all system states or references trajectories are measurable by the automation. Typical examples are partially human controlled systems, in which some subsystems are fully controlled by the automation, while others are controlled by a human. The proposed systematic design uses a novel class of games to model human-machine interaction:The near potential differential games (NPDG). We provide a necessary and sufficient condition for the existence of an NPDG and derive an algorithm for finding a NPDG which completely describes a given differential game. The proposed design method is applied to the control of a large vehicle manipulator system, in which the manipulator is controlled by the human operator and the vehicle is fully automated. The suitability of the NPDG modelling differential games is verified in simulations leading to a faster and more accurate controller design compared to manual tuning. Furthermore, the overall design process is validated in a study with sixteen test subjects indicating the applicability of the proposed concept in real applications.

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“…Among them, p−q represents the Euclidean distance between the pixel point p and the pixel point q, and their size determines the actual application effect of the bilateral filter. From the aforementioned formulas of Gaussian filter and bilateral filter, it can be seen that the weight coefficient of the Gaussian filter depends on the spatial distance, which is a fixed value in the filter window [31,32], whereas the weight coefficient of the bilateral filter depends on the space difference and pixel difference. e final filtering effect is determined by both, and hence, the weight coefficient in the filtering window is not a fixed value.…”

Section: Bilateral Filteringmentioning

confidence: 99%

Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

Zuo¹

2021

Mathematical Problems in Engineering

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Human motion recognition has an important application value in scenarios such as intelligent monitoring and advanced human-computer interaction, and it is an important research direction in the field of computer vision. Traditional human motion recognition algorithms based on two-dimensional cameras are susceptible to changes in light intensity and texture. The advent of depth sensors, especially the Kinect series with good performance and low price released by Microsoft, enables extensive research based on depth information. However, to a large extent, the depth information has not overcome these problems based on two-dimensional images. This article introduces the research background and significance of human motion recognition technology based on depth information, introduces in detail the research methods of human motion recognition algorithms based on depth information at home and abroad, and analyzes their advantages and disadvantages. The public dataset is introduced. Then, based on the depth information, a method of human motion recognition is proposed and optimized. A moving human body image segmentation method based on an improved two-dimensional Otsu method is proposed to solve the problem of inaccurate and slow segmentation of moving human body images using the two-dimensional Otsu method. In the process of constructing the threshold recognition function, this algorithm not only uses the cohesion of the pixels within the class but also considers the maximum variance between the target class and the background class. Then, the quantum particle swarm algorithm is used to find the optimal threshold solution of the threshold recognition function. Finally, the optimal solution is used to achieve accurate and fast image segmentation, which increases the accuracy of human body motion tracking by more than 30%.

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Inverse discounted-based LQR algorithm for learning human movement behaviors

Cited by 17 publications

References 33 publications

Human-behavior learning: A new complementary learning perspective for optimal decision making controllers

Human-behavior learning: A new complementary learning perspective for optimal decision making controllers

Limited Information Shared Control: A Potential Game Approach

Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

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