Robot Learning By Visual Observation

This book presents programming by demonstration for robot learning from observations with a focus on the trajectory level of task abstraction Discusses methods for optimization of task reproduction, such as reformulation of task planning as a constrained optimization problem Focuses on regression approaches, such as Gaussian mixture regression, spline regression, and locally weighted regression Concentrates on the use of vision sensors for capturing motions and actions during task demonstration by a human task expert

This book presents programming by demonstration for robot learning from observations with a focus on the trajectory level of task abstraction Discusses methods for optimization of task reproduction, such as reformulation of task planning as a constrained optimization problem Focuses on regression approaches, such as Gaussian mixture regression, spline regression, and locally weighted regression Concentrates on the use of vision sensors for capturing motions and actions during task demonstration by a human task expert

This book gathers high-quality papers presented at the International Symposium on Optomechatronic Technology (ISOT 2018), which was organized by the International Society for Optomechatronics (ISOM) and Centro de Investigaciones en Óptica (CIO) in Cancun, Mexico on November 5–8, 2018. The respective papers address the evolution of optomechatronic devices and systems, and their implementation in problem-solving and various other applications. Moreover, they cover a broad range of topics at the interface of optical, mechanical and electrical technologies and methods.

This book presents the proceedings of the 32nd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD), held in Bled, Slovenia, June 14-16, 2023. It gathers contributions by researchers from several countries on all major areas of robotic research, development and innovation, as well as new applications and current trends. The topics covered include: novel designs and applications of robotic systems, intelligent cooperating and service robots, advanced robot control, human-robot interfaces, robot vision systems, mobile robots, humanoid and walking robots, bio-inspired and swarm robotic systems, aerial, underwater and spatial robots, robots for ambient assisted living, medical robots and bionic prostheses, cognitive robots, cloud robotics, ethical and social issues in robotics, etc. Given its scope, the book offers a source of information and inspiration for researchers seeking to improve their work and gather new ideas for future developments.

Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation. While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems. Machine learning, when applied to robotics, is situated: it is embedded into a real-world system that tightly integrates perception, decision making and execution. Since robot learning involves decision making, there is an inherent active learning issue. Robotic domains are usually complex, yet the expense of using actual robotic hardware often prohibits the collection of large amounts of training data. Most robotic systems are real-time systems. Decisions must be made within critical or practical time constraints. These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning. On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution. Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).

This book focuses on two challenges posed in robot control by the increasing adoption of robots in the everyday human environment: uncertainty and networked communication. Part I of the book describes learning control to address environmental uncertainty. Part II discusses state estimation, active sensing, and complex scenario perception to tackle sensing uncertainty. Part III completes the book with control of networked robots and multi-robot teams. Each chapter features in-depth technical coverage and case studies highlighting the applicability of the techniques, with real robots or in simulation. Platforms include mobile ground, aerial, and underwater robots, as well as humanoid robots and robot arms. Source code and experimental data are available at http://extras.springer.com. The text gathers contributions from academic and industry experts, and offers a valuable resource for researchers or graduate students in robot control and perception. It also benefits researchers in related areas, such as computer vision, nonlinear and learning control, and multi-agent systems.

This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics. The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI).

The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.