Reinforcement Learning of Manipulation and Grasping Using Dynamical Movement Primitives for a Humanoidlike Mobile Manipulator

Li, Zhijun; Zhao, Ting; Chen, Fei; Hu, Yidan; Su, Chun‐Yi; Fukuda, Toshio

doi:10.1109/tmech.2017.2717461

Cited by 164 publications

(70 citation statements)

References 33 publications

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“…One of the most significant benefits of the bio-inspired human-to-robot impedance feature transfer is that adaptive impedance control for robotic arms can be realized, which has demonstrated better performance than position control or invariant impedance control for in-contact tasks by a number of works (e.g., [4,14,15,20,21]). In [22], a learning framework was established for achieving variable impedance control for robots. However, the variable impedance profiles are obtained via a time-consuming process which may limit the framework's functionality available to real world applications.…”

Section: Introductionmentioning

confidence: 99%

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Zeng

Yang

Cheng

et al. 2021

IEEE Trans. Ind. Inf.

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Section: Introductionmentioning

confidence: 99%

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Zeng

Yang

Cheng

et al. 2021

IEEE Trans. Ind. Inf.

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“…The test shows that the robot can learn how to open and drive through a door. In Reference [25], the authors use reinforcement learning strategy for a humanoid-like mobile manipulator. The strategy includes a high-level online redundancy resolution based on the neural-dynamic optimization algorithm in operational space and a low-level RL in joint space based on the dynamic movement primitives.…”

Section: Reinforcement Learning For Manipulationmentioning

confidence: 99%

Learning Mobile Manipulation through Deep Reinforcement Learning

Wang

Zhang

Tian

et al. 2020

Sensors

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Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that deep reinforcement learning is a powerful technique for fixed-base manipulation tasks, most of them are not applicable to mobile manipulation. This paper investigates how to leverage deep reinforcement learning to tackle whole-body mobile manipulation tasks in unstructured environments using only on-board sensors. A novel mobile manipulation system which integrates the state-of-the-art deep reinforcement learning algorithms with visual perception is proposed. It has an efficient framework decoupling visual perception from the deep reinforcement learning control, which enables its generalization from simulation training to real-world testing. Extensive simulation and experiment results show that the proposed mobile manipulation system is able to grasp different types of objects autonomously in various simulation and real-world scenarios, verifying the effectiveness of the proposed mobile manipulation system.

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“…In order to address the above questions, much recent work has focused on dynamic movement primitives (DMPs) [17][18][19][20][21][22], which offer a simple and versatile framework to represent and generate related movements. The core of DMPs is learning from demonstration (LfD).…”

Section: Introductionmentioning

confidence: 99%

Learning, Generalization, and Obstacle Avoidance with Dynamic Movement Primitives and Dynamic Potential Fields

et al. 2019

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In order to offer simple and convenient assistance for the elderly and disabled to take care of themselves, we propose a general learning and generalization approach for a service robot to accomplish specified tasks autonomously in an unstructured home environment. This approach firstly learns the required tasks by learning from demonstration (LfD) and represents the learned tasks with dynamic motion primitives (DMPs), so as to easily generalize them to a new environment only with little modification. Furthermore, we integrate dynamic potential field (DPF) with the above DMPs model to realize the autonomous obstacle avoidance function of a service robot. This approach is validated on the wheelchair mounted robotic arm (WMRA) by performing serial experiments of placing a cup on the table with an obstacle or without obstacle on its motion path.

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Reinforcement Learning of Manipulation and Grasping Using Dynamical Movement Primitives for a Humanoidlike Mobile Manipulator

Cited by 164 publications

References 33 publications

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Learning Mobile Manipulation through Deep Reinforcement Learning

Learning, Generalization, and Obstacle Avoidance with Dynamic Movement Primitives and Dynamic Potential Fields

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