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Learn to climb: teaching a reinforcement learning agent the single rope ascending technique

Learn to climb: teaching a reinforcement learning agent the ...

引用

International symposium on computational intelligence and Informatics

作者： Balázs Varga Department of Control for Transportation and Vehicle Systems Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9798350398830

Single rope ascending technique is used in industrial alpinism, forestry, or various leisure activities. This paper presents a multi-body model of this technique involving an actuated 3D model of a humanoid, the climbing gear, and the rope, modeled as a finite-element object. This model serves as a training ground for reinforcement learning agents trying to mimic humans in rope climbing. To demonstrate the environment, an agent with a state-of-the-art reinforcement learning algorithm (Soft Actor-Critic) was trained. Results suggest that the agent can learn how to ascend the rope with speed comparable to real humans. However, the learned technique is not human-like: the artificial agent uses its arms excessively to climb, which would be too tiring for a human. That is because the environment only rewards ascension and does not penalize the energy used. The presented learning environment is developed for humanoid robots in mind that can perform complex tasks while on the rope and can carry much heavier payloads compared to climbing robots in the literature.

关键词： Training Solid modeling Three-dimensional displays Mechatronics Gears Humanoid robots Reinforcement learning

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21st ieee International symposium on computational intelligence and Informatics, CINTI 2021 - Proceedings

21st IEEE International Symposium on Computational Intellige...

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21st ieee International symposium on computational intelligence and Informatics, CINTI 2021

ISBN: (纸本)9781665426848

The proceedings contain 43 papers. The topics discussed include: simulation aspects and challenges for space robotic systems;optimized class decomposition for fault detection;bacterial evolutionary algorithm-based feature selection for word sentiment interpolation in Hungarian language;contextual integration of activities in virtual and field operating cyber physical systems;bifurcations in a closed-loop model of tumor growth control;applying genetic programming for the inverse Linden-Mayer problem;and development of machine learning based colorectal cancer subtype classificator.

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Command Filtered Tracking control for High-order Systems with Limited Transmission Bandwidth

Command Filtered Tracking Control for High-order Systems wit...

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ieee International Conference on Robotics and automation (ICRA)

作者： Bao, Jialei Liu, Peter Xiaoping Wang, Huanqing Zheng, Minhua Zhao, Ying Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Bohai Univ Sch Math Jinzhou 121000 Peoples R China

ISBN: (纸本)9781728190778

This paper investigates the tracking control problem of a class of high-order distributed systems subjected to limited communication bandwidth. An event-triggered control method is proposed, where the controller is triggered only when specific events happen. Moreover, the computational complexity is reduced by introducing command filters for virtual control signals. Specifically, the backstepping scheme is adopted as the main design framework, by which the n-th order nonlinear system is divided into n command-cascaded first order subsystems. And virtual control commands are sent through a second-order low-pass filter, by which the time derivatives of the virtual commands can be obtained directly. The theoretical analysis shows the stability of the proposed method. The tracking performance is illustrated by a simulation example.

关键词： Backstepping Conferences computational modeling Decentralized control Low-pass filters Bandwidth Stability analysis

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A Strategic Sensor Placement for a Smart Farm Water Sprinkler System: A computational Model 24

A Strategic Sensor Placement for a Smart Farm Water Sprinkle...

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24th International Conference on Advanced Communication Technology (ICACT) - Artificial intelligence Technologies toward Cybersecurity

作者： Baek, Jinsuk Kanampiu, Munene W. Winston Salem State Univ Dept Comp Sicece Winston Salem NC 27110 USA

ISBN: (纸本)9791188428090

Internet of Things (IoT) networking has attracted research with many emerging applications requiring remote control and automation. Effective deployment of IoT sensors is a major concern since it primarily determines the performance of the IoT network. Since multiple mobile sensors are generally involved, it is possible that the sensors are randomly distributed in a remote region at the initial phase then later relocated to some pre-computed optimal location with their full autonomy enabled. In this paper, we propose a computation for the optimal location of water sprinkler sensors of an IoT smart farm network in terms of the relative physical distance between them. The resulting sensors locations ensure minimal overlap coverage area and no uncovered area exists in the candidate farming region. With the proposed strategic deployment of smart water sprinklers sensors, farmers can be assured of the right water distribution for any given area of their farm.

关键词： smart farm IoT water sprinkler deployment

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Deterministic Policy Gradient based Reinforcement Learning for Current control of Hybrid Active Power Filter

Deterministic Policy Gradient based Reinforcement Learning f...

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ieee International symposium on Circuits and Systems (ISCAS)

作者： Cheng Gong Chio-Hong Leong Chi-Seng Lam State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

The hybrid active power filter (HAPF) emerges as a cost-effective remedy for power quality challenges in medium voltage power systems. The success of HAPF, crucially, hinges on the efficacy of its current control mechanism. This paper introduces a novel approach by proposing a deterministic policy gradient based reinforcement learning (DPG-RL) as the current control strategy for HAPF. In stark contrast to conventional model-based control methods, the DPG-RL leverages artificial intelligence (AI) technology, rendering it model-free and capable of dynamically seeking the optimal control policy to enhance HAPF performance. A notable advantage lies in its significantly lower computational burden during each sampling period, distinguishing it from other contemporary AI-aided control methods. The paper outlines a systematic design process, encompassing feature selection and reward function design, to formulate the RL problem. The comprehensive design procedure of DPG-RL is then detailed. Simulation results are subsequently presented, validating the effectiveness and reliability of the proposed DPG-RL across diverse operational scenarios.

关键词： Current control Systematics computational modeling Simulation Optimal control Reinforcement learning Active filters

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Redundant robot control with learning from expert demonstrations

Redundant robot control with learning from expert demonstrat...

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ieee symposium Series on computational intelligence (SSCI)

作者： Jorge Ramirez Wen Yu Departamento de Control Automático CINVESTAV-IPN (National Polytechnic Institute) Mexico City Mexico Departamento de Control Automático CINVESTAV-IPN Mexico City Mexico

ISBN: (纸本)9781665487696

This paper proposes a biased exploration based reinforcement learning, which uses expert experiences to avoid the exploration of all states. The method is applied to control redundant robots with expert experiences. A 7-degree-of-freedom robot manipulator is used in experiments. The results show that expert demonstrations based robot control works well.

关键词： Robot control Reinforcement learning Manipulators Safety Behavioral sciences Task analysis computational intelligence

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Sensorless control Strategy for Magnetic Drive-Trains Based on Adaptive Nonlinear State Observer

Sensorless Control Strategy for Magnetic Drive-Trains Based ...

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ieee International symposium on Industrial Electronics (ISIE)

作者： Qiming Zhong Qipeng Tang Sang Xu Xin Luo Anwen Shen School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology

The magnetic drive-trains (MDTs) is widely employed in the various industrial fields because of its advantage of non-contact torque transfer. However, due to the non-linear torque transfer characteristics of permanent magnet couplings (PMC), the performance of the commonly used sensorless control strategy based on linear state observer dramatically decreases when it deviates from the linearization point. To solve this problem, a new sensorless control strategy based on adaptive nonlinear state observer is presented. By introducing an adjustable model, the system is transformed into a non-linear error feedback system consisting of linear forward and non-linear feedback paths. An asymptotically adaptive nonlinear stable observer is constructed by introducing an error feedback compensation matrix into the forward path and designing an appropriate adaptive law for the feedback path based on Popov stability criterion. The real-time observation of magnetic torque stiffness factor is achieved, and the estimation accuracy of electrical slip angle and load-side speed is improved. Finally, simulation experiments verify the validity and accuracy of the proposed scheme in load-side speed estimation.

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Gait tracking control of biped robot based on adaptive gait switching algorithm 12

Gait tracking control of biped robot based on adaptive gait ...

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12th International Conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2022

作者： Yu, Jianjun Li, Ruiqi Gong, Daoxiong Liu, Yixin Liu, Peng Beijing Key Laboratory of Computational Intelligence and Intelligent Systems Beijing University of Technology Beijing100020 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In order to make the walking gait of biped robot more human like, this paper takes the human walking data as the expected gait of robot, and uses the periodic characteristics of gait, proposes a gait tracking control strategy of Biped Robot Based on adaptive gait switching algorithm. Firstly, this paper establishes the complete dynamic models of left leg support phase (LSP) and right leg support phase (RSP) based on Lagrange method, then designs the corresponding LQR gait tracking control strategy, and uses the adaptive weighted particle swarm algorithm (A WPSO) to obtain the optimal controller parameters. Finally, the threshold range of plantar contact force in two periods are estimated based on the adaptive mechanism, and the occurrence of gait switching is detected according to the defined decision rules, thus trigger the control strategy in the next stage to realize the walking tracking control of biped robot. The experimental results show that only two LQR controllers to realize the accurate tracking of the desired gait of the biped robot, and the maximum gait speed reaches two steps/s, which is close to the human gait speed. Compared with other methods, the gait is more human like. © 2022 ieee.

关键词： Robots

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Using Custom X-vectors for the Automatic Screening of COVID-19 Based on Coughing Audio Samples

Using Custom X-vectors for the Automatic Screening of COVID-...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： José Vicente Egas-López Gábor Gosztolya ELRN-SZTE Research Group on Artificial Intelligence Szeged Hungary Institute of Informatics University of Szeged Szeged Hungary

A lot of effort has gone into eradicating the pandemic caused by the COVID-19 outbreak. One initiative in the efficient control of the spread of it lies in the methods for its diagnosis. Numerous techniques for screening the disease have emerged to date, which, combined with social measures, have helped to diminish the spread. Nevertheless, two years after the outbreak, the virus continues to propagate and claim victims worldwide. Therefore, there is a need for inexpensive, efficient, and real-time screening methods. In this scenario, the use of coughing samples as audio signals is a potential way to provide clinicians with an automatic tool for pre-diagnosing COVID-19 using AI techniques. This study investigates the use of coughutterances of subjects for the automatic detection of COVID-19. Relying on x-vector embeddings obtained from custom-trained deep neural network extractors on cough audio recordings, we were able to get highly competitive classification performance. Furthermore, we analyze the sensitivity of the extractors to domain dependence; and the quality of the embeddings produced in this context.

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Decentralized Connectivity Maintenance with Time Delays using control Barrier Functions

Decentralized Connectivity Maintenance with Time Delays usin...

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ieee International Conference on Robotics and automation (ICRA)

作者： Capelli, Beatrice Fouad, Hassan Beltrame, Giovanni Sabattini, Lorenzo Univ Modena & Reggio Emilia Dept Sci & Methods Engn DISMI Modena Italy Ecole Polytech Montreal Dept Comp & Software Engn Montreal PQ Canada

ISBN: (纸本)9781728190778

Connectivity maintenance is crucial for the real world deployment of multi-robot systems, as it ultimately allows the robots to communicate, coordinate and perform tasks in a collaborative way. A connectivity maintenance controller must keep the multi-robot system connected independently from the system's mission and in the presence of undesired real world effects such as communication delays, model errors, and computational time delays, among others. In this paper we present the implementation, on a real robotic setup, of a connectivity maintenance control strategy based on control Barrier Functions. During experimentation, we found that the presence of communication delays has a significant impact on the performance of the controlled system, with respect to the ideal case. We propose a heuristic to counteract the effects of communication delays, and we verify its efficacy both in simulation and with physical robot experiments.

关键词： Machine learning algorithms Robot kinematics Heuristic algorithms Delay effects computational modeling Maintenance engineering Delays

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