Research on robot arm control based on Unity3D machine learning

Lin, Mu; Shan, Lijun; Zhang, Yongchun

doi:10.1088/1742-6596/1633/1/012007

Cited by 10 publications

(8 citation statements)

References 1 publication

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“…The inclusion of contextual limitations, however, usually slows learning down because RL learning frequently includes a large degree of trial and error. It is vital to use virtual environments because of these RL method's drawbacks [20]. These environments enable agents to conduct trialand-error procedures in a quick and risk-free manner.…”

Section: Existing Workmentioning

confidence: 99%

A proximal policy optimization with curiosity algorithm for virtual drone navigation

Das,

Khan,

Paul

2024

Eng. Res. Express

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The drone sector is witnessing a surge in demand for advanced models tailored to address critical applications such as disaster management and intelligent warehouse deliveries. Employing simulation-based experiments with virtual drone navigation is considered a best practice before deploying physical models. Nonetheless, the current state-of-the-art virtual drone navigation system lacks accuracy and introduces notable increments in simulation time. In order to mitigate these issues, this paper introduces a deep reinforcement learning-based drone agent, designed to autonomously navigate within a constrained virtual environment. The proposed drone agent utilizes realistic drone physics in order to ensure flight within the virtual environment. The work uniquely combines & optimizes both control algorithms and physical dynamics, making the model more robust and versatile than others. The integration of curiosity-driven learning with physics-based modeling potentially increases the model’s readiness for real-world application, compared to theoretical approaches. The extensive simulation results validate the remarkable speed and accuracy of the proposed scheme compared to baseline works. The trained agent exhibits strength and versatility, enabling it to deal with the numerous targets and obstacles encountered in human environments.

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Section: Existing Workmentioning

confidence: 99%

A proximal policy optimization with curiosity algorithm for virtual drone navigation

Das,

Khan,

Paul

2024

Eng. Res. Express

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“…He also proposed a new adaptive control algorithm based on human characteristics to handle large and heavy materials and objects in a wide range of industries to improve human-robot interactions (HRIs) and system performance. Lin et al [114] conducted a study on the control strategy of a robot arm based on Unity3D machine learning as shown in Figure 10b. They used a deep reinforcement learning strategy to train the robotic arm with a reward function to achieve machine learning and intelligent control of the robotic arm.…”

Section: Machine Learning Compliance Controlmentioning

confidence: 99%

“…Soriano [111] String-level neural network Uncertain compensation Connolly [110] Multi-layer forward neural network Constraint matrix He [112] Fuzzy neural network control Flexible constraint Machine learning control Rahman [113] Reinforcement learning Human-Machine interaction Lin [114] Deep reinforcement learning Intelligent learning…”

Section: Neural Network Controlmentioning

confidence: 99%

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A Review of End-Effector Research Based on Compliance Control

Dai

Xiang

et al. 2022

Machines

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The end-effector is a key device for direct contact and operation between the operator and the workpiece, and its mechanical structure will directly affect the quality of the machine and expand its application. The theoretical research and technical implementation of the end-effector for compliance control are facing a lot of urgent challenges to be solved, therefore, the research results of active compliance control of robot end-effectors have a very broad application prospect. This paper describes the design and research results of different end-effectors under impedance-based control, hybrid force/position control, and intelligent flexible control methods, respectively. Under each control method, the structural characteristics and the optimized control scheme under different drives are introduced. Finally, key techniques for achieving compliance control are derived by summarizing, which broadens the engineering applications and provides methods and ideas for future research.

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“…The process is known for developmental psychology to solve the problem of collecting the enormous amount of required training samples in a realistic time that surpasses the possibilities of many robotic platforms [21]. Unity 3D machine learning-based research on robot arm control includes DRL strategies to train the robotic arm through machine learning, using the reward function for intelligent control of the robotic arm [22]. The authors in [13] introduced a new approach to promote the exchange of objects between robots and humans.…”

Section: Grasp Taskmentioning

confidence: 99%

Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping

et al. 2021

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While working side-by-side, humans and robots complete each other nowadays, and we may say that they work hand in hand. This study aims to evolve the grasping task by reaching the intended object based on deep reinforcement learning. Thereby, in this paper, we propose a deep deterministic policy gradient approach that can be applied to a numerous-degrees-of-freedom robotic arm towards autonomous objects grasping according to their classification and a given task. In this study, this approach is realized by a five-degrees-of-freedom robotic arm that reaches the targeted object using the inverse kinematics method. You Only Look Once v5 is employed for object detection, and backward projection is used to detect the three-dimensional position of the target. After computing the angles of the joints at the detected position by inverse kinematics, the robot’s arm is moved towards the target object’s emplacement thanks to the algorithm. Our approach provides a neural inverse kinematics solution that increases overall performance, and its simulation results reveal its advantages compared to the traditional one. The robot’s end grip joint can reach the targeted location by calculating the angle of every joint with an acceptable range of error. However, the accuracy of the angle and the posture are satisfied. Experiments reveal the performance of our proposal compared to the state-of-the-art approaches in vision-based grasp tasks. This is a new approach to grasp an object by referring to inverse kinematics. This method is not only easier than the standard one but is also more meaningful for multi-degrees of freedom robots.

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Research on robot arm control based on Unity3D machine learning

Cited by 10 publications

References 1 publication

A proximal policy optimization with curiosity algorithm for virtual drone navigation

A proximal policy optimization with curiosity algorithm for virtual drone navigation

A Review of End-Effector Research Based on Compliance Control

Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping

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