Journal ArticleDOI
Constrained motion planning of free-float dual-arm space manipulator via deep reinforcement learning
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A deep reinforcement learning based motion planning method is proposed to solve the complex constrained motion planning problem of free-floating dual-arm space manipulator.About:
This article is published in Aerospace Science and Technology.The article was published on 2021-02-01. It has received 47 citations till now. The article focuses on the topics: Motion planning & Kinematics.read more
Citations
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Journal ArticleDOI
Deep reinforcement learning with reference system to handle constraints for energy-efficient train control
TL;DR: In this article, the authors proposed an approach of DRL with a reference system (DRL-RS) for proactive constraint handling, where the reference system deals with checking and correcting the agent's learning progresses to avoid stepping farther and farther onto the erroneous road.
Journal ArticleDOI
Hybrid rigid-continuum dual-arm space robots: Modeling, coupling analysis, and coordinated motion planning
Xiangkai Ouyang,Xiangkai Ouyang,Deshan Meng,Xueqian Wang,Chengjiang Wang,Bin Liang,Ning Ding +6 more
TL;DR: A novel Hybrid Rigid-Continuum Dual-Arm Space Robot that consists of a rigid manipulator and a continuum manipulator is presented and a generalized Jacobi matrix of HRCDASR in the free-floating mode is developed for the first time.
Journal ArticleDOI
Constrained Motion Planning of 7-DOF Space Manipulator via Deep Reinforcement Learning Combined with Artificial Potential Field
TL;DR: The deep reinforcement learning algorithm is introduced to design the motion planning approach and the self-collision avoidance constraint is considered during planning to ensure the operational security.
Journal ArticleDOI
Fractional-order resolved acceleration control for free-floating space manipulator with system uncertainty
TL;DR: The proposed control method utilizes the fractional-order integral to construct a sliding surface and stirs the tracking error to converge along this surface, which improves the robustness and transient performance of the entire system.
Journal ArticleDOI
Reinforcement learning vibration control for a flexible hinged plate
TL;DR: Simulation and experimental results demonstrate that the controller trained by the proposed deep RL algorithm has better control effects compared with PD control, especially for small amplitude vibration.
References
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Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Journal ArticleDOI
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
Proceedings Article
Policy Gradient Methods for Reinforcement Learning with Function Approximation
TL;DR: This paper proves for the first time that a version of policy iteration with arbitrary differentiable function approximation is convergent to a locally optimal policy.
Journal ArticleDOI
Reinforcement learning in robotics: A survey
TL;DR: This article attempts to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots by highlighting both key challenges in robot reinforcement learning as well as notable successes.
Proceedings Article
Deterministic Policy Gradient Algorithms
TL;DR: This paper introduces an off-policy actor-critic algorithm that learns a deterministic target policy from an exploratory behaviour policy and demonstrates that deterministic policy gradient algorithms can significantly outperform their stochastic counterparts in high-dimensional action spaces.
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