Manipulation Skill Acquisition for Robotic Assembly using Deep Reinforcement Learning

Li, Fengming; Jiang, Qi; Quan, Wei; Song, Rui; Li, Yibin

doi:10.1109/aim.2019.8868579

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Zeng et al [21] proposed a transporter network, which can represent a complex multimodal strategy distribution and be extended to multistep sequential tasks and 6DOF picking and placement. Li et al [22] proposed a skill learning method based on multimodal information description. Using these multimodal information parameters, the robot can acquire the skills of searching, position determination and attitude adjustment.…”

Section: Related Workmentioning

confidence: 99%

Robot Target Location Based on the Difference in Monocular Vision Projection

Zheng

Deng

et al. 2023

IEEE Access

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Visual guidance is widely used in industrial robots. At present, the traditional method of template matching and positioning is often used in industrial robots under monocular vision guidance. However, for complex workpieces with height differences, if the angle and position of the workpiece are not consistent with the template, the projection will be different, and the positioning accuracy will be obviously reduced. A new method is proposed to solve this problem by dividing the whole contour of the workpiece and using the weighted method of the contour module to correct and match the workpiece image. First, the contour region of the template image is divided according to the position of the grasping points. Then the weight is assigned according to the distance from each contour region to the grasping points. Then, a fast feature point matching method is used to match the initial image of the workpiece to be measured. Finally, accurate contour matching is carried out for each weighted contour region so that the robot can grasp the object accurately. A large number of experiments show that the method has the design requirements of high stability and high accuracy.

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Section: Related Workmentioning

confidence: 99%

Robot Target Location Based on the Difference in Monocular Vision Projection

Zheng

Deng

et al. 2023

IEEE Access

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“…Li et al [17] used DDPG, as distinct from previous work [18] where they used Q-Learning, to perform circuit breaker assembly. In this case, they limit the maximum commanded velocity, the joint position limits, the range of the end-effector, and the range of each joint's torque parameters to guarantee safety constraints.…”

Section: Assemblymentioning

confidence: 99%

Learning and generalising object extraction skill for contact-rich disassembly tasks: an introductory study

Serrano-Muñoz

Arana-Arexolaleiba

Chrysostomou

et al. 2021

Int J Adv Manuf Technol

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Remanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the planning and operation of the processes. Machine learning methods, in particular reinforcement learning, are presented as techniques to learn, improve, and generalise the automation of many robotic manipulation tasks (most of them related to grasping, picking, or assembly). However, not much has been exploited in remanufacturing, in particular in disassembly tasks. This work presents the state of the art of contact-rich disassembly using reinforcement learning algorithms and a study about the generalisation of object extraction skills when applied to contact-rich disassembly tasks. The generalisation capabilities of two state-of-the-art reinforcement learning agents (trained in simulation) are tested and evaluated in simulation, and real world while perform a disassembly task. Results show that at least one of the agents can generalise the contact-rich extraction skill. Besides, this work identifies key concepts and gaps for the reinforcement learning algorithms’ research and application on disassembly tasks.

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“…In this paper, we focus on the most common task in robot assembly: multiple peg-in-hole assembly, which is the basis of various assembly operations. For the precise multiple peg-in-hole assembly operation with abundant contact tasks, the current research still has problems such as low efficiency and poor robustness (Li et al, 2019). In view of this, this paper will conduct research on automated multiple peg-in-hole assembly.…”

Section: Introductionmentioning

confidence: 99%

Multiple peg-in-hole compliant assembly based on a learning-accelerated deep deterministic policy gradient strategy

Xiao

Zhao

et al. 2021

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Purpose As complex analysis of contact models is required in the traditional assembly strategy, it is still a challenge for a robot to complete the multiple peg-in-hole assembly tasks autonomously. This paper aims to enable the robot to complete the assembly tasks autonomously and more efficiently, with the strategies learned by reinforcement learning (RL), a learning-accelerated deep deterministic policy gradient (LADDPG) algorithm is proposed. Design/methodology/approach The multiple peg-in-hole assembly strategy is designed in two modules: an advanced planning module and a bottom control module. The advanced module is completed by the LADDPG agent, which is used to derive advanced commands based on geometric and environmental constraints, that is, the desired contact force. The bottom-level control module will drive the robot to complete the compliant assembly task through the adaptive impedance algorithm according to the command set issued by the advanced module. In addition, a set of safety assurance mechanisms is developed to safely train a collaborative robot to complete autonomous learning. Findings The method can complete the assembly tasks well through RL, and it can realize satisfactory compliance of the robot to the environment. Compared with the original DDPG algorithm, the average values of the instantaneous maximum contact force and contact torque during the assembly process are reduced by approximately 38% and 74%, respectively. Practical implications The entire algorithm can also be applied to other robots and the assembly strategy can be applied in the field of the automatic assembly. Originality/value A compliant assembly strategy based on the LADDPG algorithm is proposed to complete the automated multiple peg-in-hole assembly tasks.

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Manipulation Skill Acquisition for Robotic Assembly using Deep Reinforcement Learning

Cited by 8 publications

References 15 publications

Robot Target Location Based on the Difference in Monocular Vision Projection

Robot Target Location Based on the Difference in Monocular Vision Projection

Learning and generalising object extraction skill for contact-rich disassembly tasks: an introductory study

Multiple peg-in-hole compliant assembly based on a learning-accelerated deep deterministic policy gradient strategy

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