Deep Reinforcement Learning-Based Robotic Grasping in Clutter and Occlusion

In robotic manipulation, object grasping is a basic yet challenging task. Dexterous grasping necessitates intelligent visual observation of the target objects by emphasizing the importance of spatial equivariance to learn the grasping policy. In this paper, two significant challenges associated with robotic grasping in both clutter and occlusion scenarios are addressed. 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. 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 second challenge is the avoidance of occlusion that occurs when the camera itself is entirely or partially occluded during a grasping action. This paper proposes a multi-view change observation-based approach (MV-COBA) to overcome these two problems. 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. 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.

Learning Future-Aware Correlation Filters for Efficient UAV Tracking

In recent years, discriminative correlation filter (DCF)-based trackers have made considerable progress and drawn widespread attention in the unmanned aerial vehicle (UAV) tracking community. Most existing trackers collect historical information, e.g., training samples, previous filters, and response maps, to promote their discrimination and robustness. Under UAV-specific tracking challenges, e.g., fast motion and view change, variations of both the target and its environment in the new frame are unpredictable. Interfered by future unknown environments, trackers that trained with historical information may be confused by the new context, resulting in tracking failure. In this paper, we propose a novel future-aware correlation filter tracker, i.e., FACF. The proposed method aims at effectively utilizing context information in the new frame for better discriminative and robust abilities, which consists of two stages: future state awareness and future context awareness. In the former stage, an effective time series forecast method is employed to reason a coarse position of the target, which is the reference for obtaining a context patch in the new frame. In the latter stage, we firstly obtain the single context patch with an efficient target-aware method. Then, we train a filter with the future context information in order to perform robust tracking. Extensive experimental results obtained from three UAV benchmarks, i.e., UAV123_10fps, DTB70, and UAVTrack112, demonstrate the effectiveness and robustness of the proposed tracker. Our tracker has comparable performance with other state-of-the-art trackers while running at ∼49 FPS on a single CPU.

Reason versus Ian Hacking’s styles of scientific reasoning

There is a view in contemporary philosophy of science according to which scientific methodology itself is subject to radical change as part of scientific progress. According to this view, change in science is not confined to accepted theories. The core principles of scientific theory appraisal, including the rules and categories used to rank and confer truth-values on theories, are also said to be subject to radical change as science develops. In this paper, I examine Ian Hacking’s (1975; 1980; 1982; 1983; 1985; 1996; 1999; 2012) version of this no-invariant-methodology thesis. I argue that, just like Thomas Kuhn’s “paradigms,” Larry Laudan’s “research traditions,” and Imre Lakatos’ “research programmes,” Hacking’s “styles of reasoning” fail to give an adequate account of scientific progress.

GSAP: A Global Structure Attention Pooling Method for Graph-Based Visual Place Recognition

The Visual Place Recognition problem aims to use an image to recognize the location that has been visited before. In most of the scenes revisited, the appearance and view are drastically different. Most previous works focus on the 2-D image-based deep learning method. However, the convolutional features are not robust enough to the challenging scenes mentioned above. In this paper, in order to take advantage of the information that helps the Visual Place Recognition task in these challenging scenes, we propose a new graph construction approach to extract the useful information from an RGB image and a depth image and fuse them in graph data. Then, we deal with the Visual Place Recognition problem as a graph classification problem. We propose a new Global Pooling method—Global Structure Attention Pooling (GSAP), which improves the classification accuracy by improving the expression ability of the Global Pooling component. The experiments show that our GSAP method improves the accuracy of graph classification by approximately 2–5%, the graph construction method improves the accuracy of graph classification by approximately 4–6%, and that the whole Visual Place Recognition model is robust to appearance change and view change.

Leadership of Organization Administrators under the Era of Technological Change

“Leadership of Organization Administrators under the Era of Technological Change” is an academic article aims to study leadership of organization administrators under the era of technological change. Being able to lead an organization, administrators must view change as a challenge or opportunity and know how to make an effective change from inside and outside organizations. They must be able to build consciousness to organizations, as leaders of change. Meanwhile, they have to enable organizations view change as a new opportunity to ensure organization management gain maximum benefits and organizations can be developed in a secure and sustainable manner.

The Context Principle in Frege’s Grundgesetze

According to Frege’s context principle, we must never to ask for the meaning of a word in isolation but only in the context of a sentence. The context principle poses some very hard interpretive challenges; not only are the ideas themselves hard, but relevant parts of Frege’s view change in the course of his career. This principle plays a crucial role in the Grundlagen, especially in Frege’s answer to the question of how numbers and other logical objects are ‘given to us’. By contrast, the context principle figures less prominently in the Grundgesetze. This article argues that, appearances to the contrary, the context principle survives in Frege’s magnum opus, where it still has an important role to play. Reflection on this role sheds important new light on the principle. More specifically, I argue that the context principle must be disentangled from the problematic idea of ‘recarving of content’; that the principle is fully compatible with Frege’s compositional semantics; and that it suggests a more ‘lightweight’ conception of mathematical objects than is typically associated with Frege.