Grasping and assembly are essential tasks in high-precision robotic manipulation for industrial manufacturing as well as for home service applications. Many efforts have been devoted to this area, in an attempt to meet the increasing precision requirement of the task. However, it remains a problematic objective to fulfill high precision, high reliability, high speed, and high flexibility all at once during one robotic manipulation task. To find answers to the above problem, this paper tries to categorize, review and compare the recent works focusing on robotic grasping and assembly tasks to reveal some potential trends in this research area. The approaches will be divided into five groups based on the difference in the utilization of sensing or constraints. For each part, the robotic grasping and assembly will be treated as practical cases to illustrate the concrete work in that area. It is hoped that this paper could give the readers some knowledge on the current development of the robotic manipulation, and could provide new thoughts on future direction in this area, inspiring new designs, structures and systems to meet new requirements in applications in industrial manufacturing and home service.

High-precision manipulation is very important for a variety of tasks in manufacturing. In general, high-precision manipulation is usually achieved by the guidance of high-precision sensors; however, sensorless methods, for some special cases, are more reliable and effective. There are many important works in this aspect [1]-[3]. The concept of “attractive region in environment” (ARIE), which was proposed in our previous works [4]-[6], provides a prospective approach for low-precision systems to achieve high-precision manipulation. It has been used in various industrial applications, such as robotic assembly [7], grasping [8], and localization [9]. ARIE is a region from any point of which the uncertainty of the system can be eliminated by a state-independent input. The utilization of attractive region can be easily understood by the case of a bean in a bowl. For a bean in a bowl, no matter where the initial position of the bean is, it will move to and finally stay at the bottom of the bowl under gravity. Thus, the uncertainty of the bean is eliminated by gravity without any sensor feedback. Inspired by the above case, the general concept of ARIE is proposed and is applied to industry. In this paper, we review the application of ARIE and give its formal definition. Furthermore, we establish conditions for the existence of ARIE in the generalized configuration space, which is complex and nonideal. The relationship between the high-dimensional attractive region and the low-dimensional one is also discussed, which is helpful to make feasible and reliable manipulation strategies in the low-dimensional configuration space. The main contribution of this paper is the generalization of the concept of ARIE, which provides a theoretical foundation for applications including the high-precision manipulation with low-precision system. The experimental simulations on different kinds of complex manipulations show the effectiveness of the proposed method for industrial applications.

Chaotic systems implemented by artificial neural networks are good candidates for data encryption. In this manner, this paper introduces the cryptographic application of the Hopfield and the Hindmarsh–Rose neurons. The contribution is focused on finding suitable coefficient values of the neurons to generate robust random binary sequences that can be used in image encryption. This task is performed by evaluating the bifurcation diagrams from which one chooses appropriate coefficient values of the mathematical models that produce high positive Lyapunov exponent and Kaplan–Yorke dimension values, which are computed using TISEAN. The randomness of both the Hopfield and the Hindmarsh–Rose neurons is evaluated from chaotic time series data by performing National Institute of Standard and Technology (NIST) tests. The implementation of both neurons is done using field-programmable gate arrays whose architectures are used to develop an encryption system for RGB images. The success of the encryption system is confirmed by performing correlation, histogram, variance, entropy, and Number of Pixel Change Rate (NPCR) tests.

In multigoal reinforcement learning (RL), algorithms usually suffer from inefficiency in the collection of successful experiences in tasks with sparse rewards. By utilizing the ideas of relabeling hindsight experience and curriculum learning, some prior works have greatly improved the sample efficiency in robotic manipulation tasks, such as hindsight experience replay (HER), hindsight goal generation (HGG), graph-based HGG (G-HGG), and curriculum-guided HER (CHER). However, none of these can learn efficiently to solve challenging manipulation tasks with distant goals and obstacles, since they rely either on heuristic or simple distance-guided exploration. In this article, we introduce graph-curriculum-guided HGG (GC-HGG), an extension of CHER and G-HGG, which works by selecting hindsight goals on the basis of graph-based proximity and diversity. We evaluated GC-HGG in four challenging manipulation tasks involving obstacles in both simulations and real-world experiments, in which significant enhancements in both sample efficiency and overall success rates over prior works were demonstrated. Videos and codes can be viewed at this link: https://videoviewsite.wixsite.com/gc-hgg .

Precision assembly is one of the primary goals of robots in manufacturing. Assembly strategies combining active and passive compliant control are presented herein. In this article, we construct a high-dimensional configuration space of robot assembly and then divide the configuration space to its subspaces. We further map the active compliant motion and the passive compliant motion of the manipulator into different subspaces. In one subspace, we construct the constraint function and design the passive compliant motion of the manipulator in the constraint region, where the uncertainties of the system should be eliminated by the environment constraints. In another subspace, we design a force controller based on the low-resolution force sensory information to control the position of the robot. The proposed method avoids the design of precision mechanism systems and the usage of high-quality sensors. Several experiments pertaining to peg-in-hole insertions are conducted to demonstrate the efficiency of the proposed method.