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For successful human-robot collaboration, it is crucial to establish and sustain quality interaction between humans and robots, making it essential to facilitate human-robot interaction (HRI) effectively. The evolution of robot intelligence now enables robots to take a proactive role in initiating and sustaining HRI, thereby allowing humans to concentrate more on their primary tasks. In this paper, we introduce a system known as the Robot-Facilitated Interaction System (RFIS), where mobile robots are employed to perform identification, tracking, re-identification, and gesture recognition in an integrated framework to ensure anytime readiness for HRI. We implemented the RFIS on an autonomous mobile robot used for transporting a patient, to demonstrate proactive, real-time, and user-friendly interaction with a caretaker involved in monitoring and nursing the patient. In the implementation, we focused on the efficient and robust integration of various interaction facilitation modules within a real-time HRI system that operates in an edge computing environment. Experimental results show that the RFIS, as a comprehensive system integrating caretaker recognition, tracking, re-identification, and gesture recognition, can provide an overall high quality of interaction in HRI facilitation with average accuracies exceeding 90% during real-time operations at 5 FPS.

An energy-efficient support vector machine (EE-SVM) learning control system considering the energy cost of each training sample of biped dynamic is proposed to realize energy-efficient biped walking. Energy costs of the biped walking samples are calculated. Then the samples are weighed with the inverses of the energy costs. An EE-SVM objective function with energy-related slack variables is proposed, which follows the principle that the sample with the lowest energy consumption is treated as the most important one in the training. That means the samples with lower energy consumption contribute more to the EE-SVM regression function learning, which highly increases the energy efficiency of the biped walking. Simulation results demonstrate the effectiveness of the proposed method.

For smart manufacturing, an automated robotic assembly system built upon an autoprogramming environment is necessary to reduce setup time and cost for robots that are engaged in frequent task reassignment. This article presents an approach to the autoprogramming of robotic assembly tasks with minimal human assistance. The approach integrates “robotic learning of assembly tasks from observation” and “robotic embodiment of learned assembly tasks in the form of skills.” In the former, robots observe human assembly operations to learn a sequence of assembly tasks, which is formalized into a human assembly script. The latter transforms the human assembly script into a robot assembly script in which a sequence of robot-executable assembly tasks are defined based on action planning supported by workspace modeling and simulated retargeting. The assembly tasks, in the form of the robot assembly script, are then implemented via pretrained robot skills. These skills aim to enable robots to execute difficult tasks that involve inherent uncertainties and variations. We validate the proposed approach by building a prototype of the automated robotic assembly system for a power breaker and an electronic set-top box. The results verify that the proposed automated robotic assembly system is not only feasible but also viable, as it is associated with a dramatic reduction in the human effort required for automating robotic assembly. [ABSTRACT FROM AUTHOR]

Recently, structured light 3D imaging devices have gained a keen attention due to their potential applications to robotics, industrial manufacturing and medical imaging. Most of these applications require high 3D precision yet high speed in image capturing for hard and/or soft real time environments. This paper presents a method of high speed image capturing for structured light 3D imaging sensors with FPGA based structured light pattern generation and projector-camera synchronization. Suggested setup reduces the time for pattern projection and camera triggering to 16msec from 100msec that should be required by conventional methods. [ABSTRACT FROM AUTHOR]

Human-like behavior is crucial for intelligent service robots that are to perform versatile tasks in day to day life. In this paper, an integrated approach to human-like manipulation is presented, which addresses realtime three-dimensional (3-D) workspace modeling and accessibility analysis for motion planning. The 3-D workspace modeling uses three main principles: identification of global geometric features, substitution of recognized known objects by corresponding solid models in the database and multi-resolution representation of unknown obstacles as required by the task at hand. Accessibility analysis is done through visibility tests. It complements and accelerates general motion planning. The experimental results demonstrate that the human-like behavior-oriented methods are sufficiently fast and robust to model 3-D workspace, and to plan and execute tasks for robotic manipulative applications. [ABSTRACT FROM AUTHOR]

Multirobot workcells provide efficiency of space, time, and materials in assembly. However, this advantage cannot be fully utilized unless an efficient off-line/on-line collision-free trajectory planner is in charge of controlling the arms. In this article a new algorithm which provides the on-line and real-time generation of dual-arm collision-free trajectories has been established. The algorithm is capable of generating dual-arm collision-free trajectories in real-time despite the fact that the initial configurations of two arms are not synchronized. The algorithm is based on two sections: collision detection and path planning. The collision detection section uses a fast geometric approach based on link intersection. The path planning section, the key component of the algorithm, uses the virtual road map which gives the best collision-free motion at each given arm configuration. © 2001 John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Automated image completion and masking have been emerged as a subject of keen interest due to their impact on image modification and interpretation. The current state-of-the-art approaches require a fixed format of missing parts and are ineffective for handling corrupted images. Besides, they focus exclusively on the image completion without taking into consideration the image masking as an inverse process of completion. This paper proposes a deep learning approach to an integrated framework of image completion and masking based on the cross-mapping generative adversarial network or CM-GAN, in short. CM-GAN offers the robustness in image completion under corruptions as well as the capability of synthesizing various masked images with arbitrary mask locations and shapes. In particular, the capability of CM-GAN in image masking is shown to be extended into the removal of unwanted backgrounds in images. We verify the superior performance of CM-GAN for image completion and masking based on extensive experiments. Furthermore, we implement a deep learning based robotic bin picking to demonstrate that the background removal capability of CM-GAN plays a key role for estimating the 3D pose of randomly filed multiple industrial parts in a bin. • A novel deep learning network, CM-GAN is presented for image completion and masking. • CM-GAN is configured in cGAN with two AEs the latent spaces of which are associated. • CM-GAN is superior in denoising power for completion and versatile in image masking. • CM-GAN is successfully applied to robotic bin picking with image background removal. [ABSTRACT FROM AUTHOR]

The article reports on the standardization and roadmapping initiatives of the Industrial Activity Board (IAB). As part of its initiatives, the IAB introduced the Standards Activities and the International Technology Roadmap for Robotics and Automation (ITTRA). The goal of this initiatives is to establish the IEEE Robotics and Automation Society as the major player for standardization for robotics and automation. ITTRA goal is to develop an efficient roadmapping process and to produce a technology roadmap for robotics and automation.

Highlights the IEEE Robotics and Automation Society Industrial Activities Board workshop. Talks given and discussions; Updates on robotics given.

Abstract: In this paper, we present visibility-based spatial reasoning techniques for real-time object manipulation in cluttered environments. When a robot is requested to manipulate an object, a collision-free path should be determined to access, grasp, and move the target object. This often requires processing of time-consuming motion planning routines, making real-time object manipulation difficult or infeasible, especially in a robot with a high DOF and/or in a highly cluttered environment. This paper places special emphasis on developing real-time motion planning, in particular, for accessing and removing an object in a cluttered workspace, as a local planner that can be integrated with a general motion planner for improved overall efficiency. In the proposed approach, the access direction of the object to grasp is determined through visibility query, and the removal direction to retrieve the object grasped by the gripper is computed using an environment map. The experimental results demonstrate that the proposed approach, when implemented by graphics hardware, is fast and robust enough to manipulate 3D objects in real-time applications. [Copyright &y& Elsevier]