Tag Archives: Review

On the definition of “action” in robotics and other fields

Philipp Zech Erwan Renaudo, Simon Haller, Xiang Zhang, Justus Piater, Action representations in robotics: A taxonomy and systematic classification, The International Journal of Robotics Research, 2019, DOI: 10.1177/0278364919835020.

Understanding and defining the meaning of “action” is substantial for robotics research. This becomes utterly evident when aiming at equipping autonomous robots with robust manipulation skills for action execution. Unfortunately, to this day we still lack both a clear understanding of the concept of an action and a set of established criteria that ultimately characterize an action. In this survey, we thus first review existing ideas and theories on the notion and meaning of action. Subsequently, we discuss the role of action in robotics and attempt to give a seminal definition of action in accordance with its use in robotics research. Given this definition we then introduce a taxonomy for categorizing action representations in robotics along various dimensions. Finally, we provide a meticulous literature survey on action representations in robotics where we categorize relevant literature along our taxonomy. After discussing the current state of the art we conclude with an outlook towards promising research directions.

An interesting review of criticisms of deep learning in cognitive science

Radoslaw M. Cichy, Daniel Kaiser, Deep Neural Networks as Scientific Models, Trends in Cognitive Sciences, Volume 23, Issue 4, 2019, Pages 305-317, DOI: 10.1016/j.tics.2019.01.009.

Artificial deep neural networks (DNNs) initially inspired by the brain enable computers to solve cognitive tasks at which humans excel. In the absence of explanations for such cognitive phenomena, in turn cognitive scientists have started using DNNs as models to investigate biological cognition and its neural basis, creating heated debate. Here, we reflect on the case from the perspective of philosophy of science. After putting DNNs as scientific models into context, we discuss how DNNs can fruitfully contribute to cognitive science. We claim that beyond their power to provide predictions and explanations of cognitive phenomena, DNNs have the potential to contribute to an often overlooked but ubiquitous and fundamental use of scientific models: exploration.

A nice review of visual SLAM with deep learning, and its evolution from non-learning visual SLAM

Ruihao Li, Sen Wang, DongBing Gu, Ongoing Evolution of Visual SLAM from Geometry to Deep Learning: Challenges and Opportunities, Cognitive Computation, December 2018, Volume 10, Issue 6, pp 875–889, DOI: 10.1007/s12559-018-9591-8.

Visual simultaneous localization and mapping (SLAM) has been investigated in the robotics community for decades. Significant progress and achievements on visual SLAM have been made, with geometric model-based techniques becoming increasingly mature and accurate. However, they tend to be fragile under challenging environments. Recently, there is a trend to develop data-driven approaches, e.g., deep learning, for visual SLAM problems with more robust performance. This paper aims to witness the ongoing evolution of visual SLAM techniques from geometric model-based to data-driven approaches by providing a comprehensive technical review. Our contribution is not only just a compilation of state-of-the-art end-to-end deep learning SLAM work, but also an insight into the underlying mechanism of deep learning SLAM. For such a purpose, we provide a concise overview of geometric model-based approaches first. Next, we identify visual depth estimation using deep learning is a starting point of the evolution. It is from depth estimation that ego-motion or pose estimation techniques using deep learning flourish rapidly. In addition, we strive to link semantic segmentation using deep learning with emergent semantic SLAM techniques to shed light on simultaneous estimation of ego-motion and high-level understanding. Finally, we visualize some further opportunities in this research direction.

A review on mobile robot navigation

Tzafestas, S.G. , Mobile Robot Control and Navigation: A Global Overview,J Intell Robot Syst (2018) 91: 35 DOI: 10.1007/s10846-018-0805-9.

The aim of this paper is to provide a global overview of mobile robot control and navigation methodologies developed over the last decades. Mobile robots have been a substantial contributor to the welfare of modern society over the years, including the industrial, service, medical, and socialization sectors. The paper starts with a list of books on autonomous mobile robots and an overview of survey papers that cover a wide range of decision, control and navigation areas. The organization of the material follows the structure of the author’s recent book on mobile robot control. Thus, the following aspects of wheeled mobile robots are considered: kinematic modeling, dynamic modeling, conventional control, affine model-based control, invariant manifold-based control, model reference adaptive control, sliding-mode control, fuzzy and neural control, vision-based control, path and motion planning, localization and mapping, and control and software architectures.

A nice review on the topic of active perception

Ruzena BajcsyYiannis AloimonosJohn K. Tsotsos, Revisiting active perception, Auton Robot (2018) 42: 177, DOI: 10.1007/s10514-017-9615-3.

Despite the recent successes in robotics, artificial intelligence and computer vision, a complete artificial agent necessarily must include active perception. A multitude of ideas and methods for how to accomplish this have already appeared in the past, their broader utility perhaps impeded by insufficient computational power or costly hardware. The history of these ideas, perhaps selective due to our perspectives, is presented with the goal of organizing the past literature and highlighting the seminal contributions. We argue that those contributions are as relevant today as they were decades ago and, with the state of modern computational tools, are poised to find new life in the robotic perception systems of the next decade.