![]() To introduce this topic, we first review the fundamentals of reinforcement learning and various methods of policy optimization, such as value iteration and policy search. This survey reviews the application of reinforcement learning for pick-and-place operations, a task that a logistics robot can be trained to complete without support from a robotics engineer. The field of robotics has been rapidly developing in recent years, and the work related to training robotic agents with reinforcement learning has been a major focus of research. According to experimental simulation findings, the proposed approach achieved an average grasp success rate of 83.6%, 86.3%, and 97.8% in the cluttered, well-ordered object, and occlusion scenarios, respectively. The proposed approach is divided into two parts: 1) using multiple cameras to set up multiple views to address the occlusion issue and 2) using visual change observation on the basis of the pixel depth difference to address the challenge of coordinating push and grasp actions. This paper proposes a multi-view change observation-based approach (MV-COBA) to overcome these two problems. The second challenge is the avoidance of occlu-sion that occurs when the camera itself is entirely or partially occluded during a grasping action. On the other hand, when employed in a randomly cluttered object scenario, the pushing behavior may be less efficient, as many objects are more likely to be pushed out of the workspace. The first challenge is the coordination of push and grasp actions, in which the robot may occasionally fail to disrupt the arrangement of the objects in a well-ordered object scenario. In this paper, two significant challenges associated with robotic grasping in both clutter and occlusion scenarios are addressed. Dexterous grasping necessitates intelligent visual observation of the target objects by emphasizing the importance of spatial equivariance to learn the grasping policy. In robotic manipulation, object grasping is a basic yet challenging task. Finally, open problems and future research directions are summarised. ![]() Typical industrial applications in robotic grasping, assembly, process control, and industrial human-robot collaboration are listed and discussed. Training tools, benchmarks, and comparisons amongst different robot learning methods are delivered. Concretely, imitation learning, policy gradient learning, value function learning, actor-critic learning, and model-based learning as the leading technologies in robot learning are reviewed. Definitions and categories of robot learning are sum-marised. Towards the next generation of manufacturing, this review first introduces the comprehensive background of smart robotic manufacturing within robotics, machine learning, and robot learning. Since the beginning of the first integration of industrial robots into production lines, industrial robots have enhanced productivity and relieved humans from heavy workloads significantly. Robotic equipment has been playing a central role since the proposal of smart manufacturing. Index Term-Robotic grasp-to-place system, pixel-wise Q-value, Deep Reinforcement Learning (DRL), Q-learning, DenseNet. As for place task, our framework successfully placed objects with a place success rate to at least 90 % through all test-cases. In the simulation, exhaustive experimental results demonstrate that our framework successfully grasp objects with a grasp success rate and grasp efficiency, at almost 80~95% for both. This effectively gives us values for predictions for 36 different grasping angles for every visible location in the robot grasp-workspace. Q-values are a measure of future expected reward in the formula of Q-learning from reinforcement learning. The heightmap is rotated by 36 different angles before feeding into the network in order to generate a set of 36 pixel-wise Q-value maps, which is then passed into a Dense Network (DenseNet) to generate predictions of Q-values. ![]() ![]() To achieve this, the camera captured RGB images of the scene, and 3d point cloud information are used to generate heightmaps at the robot grasp-workspace. Thus, the contribution of this paper is to model such a complete manipulation system with reasonable computational complexity. The key feature of the system is that it handles both primitive actions of picking and placing of objects with an explicit framework using raw RGB-D images. ![]() This paper presents a robotic grasp-to-place system that has the capability of grasping objects in sparse and cluttered environments. ![]()
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