Search Results - Optimization and Optimal Control Reinforcement Learning Machine Learning for Robot Control

2-DOF Robot Optimal Control via Artificial Neural Network Reinforcement Learning by Romasevych, Yuriy, Loveikin, Viatcheslav

“…In the study optimal control problem a 2-DOF robot is stated. Criterion, which must be minimized, reflected RMS of drives power and length of the load trajectory in the "x- y" plane…”
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Robot Arm Movement Control by Model-based Reinforcement Learning using Machine Learning Regression Techniques and Particle Swarm Optimization by Mueangprasert, Meta, Chermprayong, Pisak, Boonlong, Kittipong

“… The robot arm movement control is important in the use of robot arms. This paper presents model-based reinforcement learning (MBRL…”
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Robot Control Optimization Using Reinforcement Learning by Song, Kai-Tai, Sun, Wen-Yu

“… This paper proposes a reinforcement learning control approach for overcoming such drawbacks…”
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Deep Reinforcement Learning for Robot Batching Optimization and Flow Control by Hildebrand, Max, Andersen, Rasmus S., Bøgh, Simon

“…Robot batching is an optimization problem found in many industrial applications. Current state-of-the-art approaches utilize a combination of heuristic based parameters and statistical analysis…”
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Neural Networks Enhanced Optimal Admittance Control of Robot-Environment Interaction Using Reinforcement Learning by Peng, Guangzhu, Chen, C. L. Philip, Yang, Chenguang

“…In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments…”
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Optimal control of a two‐wheeled self‐balancing robot by reinforcement learning by Guo, Linyuan, Rizvi, Syed Ali Asad, Lin, Zongli

“…Summary This article concerns optimal control of the linear motion, tilt motion, and yaw motion of a two‐wheeled self‐balancing robot (TWSBR…”
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Optimal balancing control of bipedal robots using reinforcement learning by Fang Peng, Lijia Ding, Zhijun Li, Chenguang Yang, Chun-Yi Su

“…The balance control a bipedal robot in the presence of external disturbances is still a challenge…”
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Agile Legged Robots Through Reinforcement Learning and Optimal Control by Yang, Yuxiang

“… Traditional optimal control methods, which rely on predefined physics models to optimize motor commands, can precisely track desired motions but cannot plan for complex, long-horizon trajectories due…”
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Optimization Control of Attitude Stability for Hexapod Robots Based on Reinforcement Learning by Ji, Shujie, Wei, Wu, Liu, Xiongding, Wu, Junqi

“…To enhance the attitude stability of hexapod robots on rough terrain, this paper introduces a layered modular motion control method, termed reinforcement learning integrated with foot impedance…”
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Online parameter adaptive control of mobile robots based on deep reinforcement learning under multiple optimisation objectives by Sui, Xiuli, Chen, Haiyong

“…Fixed control parameters and various optimisation objectives significantly limit the robot control performance…”
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Self-optimization composite dynamic control for trajectory tracking of robot manipulators via deep reinforcement learning by Kuang, Chenyang, Han, Linyan, Liang, Kun, Mao, Jianliang, Zhang, Chuanlin

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An Admittance Parameter Optimization Method Based on Reinforcement Learning for Robot Force Control by Hu, Xiaoyi, Liu, Gongping, Ren, Peipei, Jia, Bing, Liang, Yiwen, Li, Longxi, Duan, Shilin

“… control method, which combines classical adaptive control and machine learning methods to make them use their respective advantages in different stages of training and, ultimately, achieve better performance…”
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Decentralized Reinforcement Learning Robust Optimal Tracking Control for Time Varying Constrained Reconfigurable Modular Robot Based on ACI and Q-Function by Dong, Bo, Li, Yuanchun

“…A novel decentralized reinforcement learning robust optimal tracking control theory for time varying constrained reconfigurable modular robots based on action-critic-identifier (ACI…”
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Reinforcement Learning Control of an Aerial Robot Based on a Tuned Proximal Policy Optimization in Takeoff and Hover Phases by Esfandiari, Mohamadamin, Amiri Atashgah, M. A.

“…This paper is dedicated to the implementation of a control system for a flying robot based on reinforcement learning (RL…”
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Kernel-based multiagent reinforcement learning for near-optimal formation control of mobile robots by Zhang, Ronghua, Xu, Xin, Zhang, Xinglong, Xiong, Quan, Ma, Qingwen, Peng, Yaoqian

“… In this paper, a kernel-based multiagent reinforcement learning (KMARL) algorithm for formation control of wheeled mobile robots with nonholonomic constraints is proposed…”
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Optimal Gait Control for a Tendon-Driven Soft Quadruped Robot by Model-Based Reinforcement Learning by Niu, Xuezhi, Tan, Kaige, Broo, Didem Gurdur, Feng, Lei

“…This study presents an innovative approach to optimal gait control for a soft quadruped robot enabled by four compressible tendon-driven soft actuators…”
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Fault Estimate and Reinforcement Learning Based Optimal Output Feedback Control for Single-Link Robot Arm Model by Liu, Sihan, Yan, Hailong, Zhao, Lixia, Gao, Dongxiang

“…This paper presents an optimal output feedback tracking control scheme for a single-link robot arm (SLRA…”
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Adaptive Prescribed-Time Optimal Control for Flexible-Joint Robots via Reinforcement Learning by Xie, Shiyu, Sun, Wei, Sun, Yougang, Su, Shun-Feng

“…) robot systems utilizing the reinforcement learning (RL) strategy. The uniqueness of this method lies in its ability to ensure optimal tracking performance for n-link…”
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Reinforcement Learning for Robot Optimal Impedance Control: Theoretical Feasibility and Extensibility Analysis by Wu, Han, Hu, Qinglei, Zheng, Jianying, Shao, Xiaodong, Liu, Yueyang, Li, Dongyu

“… Reinforcement learning (RL) has been proven effective and widely exploited in robot control. However, existing studies lack attention to the theoretical feasibility of RL in contact control and its extensibility to different environment models…”
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Optimal Formation Control for Mobile Robots Under Prescribed Performance Constraints via Reinforcement Learning by Lin, Kai, Li, Tian, Dai, Shi-Lu

“…This study introduces an optimal control algorithm for the formation of mobile robots, addressing collision avoidance and adhering to specified performance constraints…”
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