Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions.

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Imitation Learning: A Survey of Learning Methods

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.

Modelling and Control of Robot Manipulators

Robot assistants and professional coworkers are becoming a commodity in domestic and industrial settings. In order to enable robots to share their workspace with humans and physically interact with them, fast and reliable handling of possible collisions on the entire robot structure is needed, along with control strategies for safe robot reaction. The primary motivation is the prevention or limitation of possible human injury due to physical contacts. In this survey paper, based on our early work on the subject, we review, extend, compare, and evaluate experimentally model-based algorithms for real-time collision detection, isolation, and identification that use only proprioceptive sensors. This covers the context-independent phases of the collision event pipeline for robots interacting with the environment, as in physical human–robot interaction or manipulation tasks. The problem is addressed for rigid robots first and then extended to the presence of joint/transmission flexibility. The basic physically motivated solution has already been applied to numerous robotic systems worldwide, ranging from manipulators and humanoids to flying robots, and even to commercial products.

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Robot Collisions: A Survey on Detection, Isolation, and Identification

Recent technological advances in hardware design of the robotic platforms enabled the implementation of various control modalities for improved interactions with humans and unstructured environments. An important application area for the integration of robots with such advanced interaction capabilities is human---robot collaboration. This aspect represents high socio-economic impacts and maintains the sense of purpose of the involved people, as the robots do not completely replace the humans from the work process. The research community's recent surge of interest in this area has been devoted to the implementation of various methodologies to achieve intuitive and seamless human---robot-environment interactions by incorporating the collaborative partners' superior capabilities, e.g. human's cognitive and robot's physical power generation capacity. In fact, the main purpose of this paper is to review the state-of-the-art on intermediate human---robot interfaces (bi-directional), robot control modalities, system stability, benchmarking and relevant use cases, and to extend views on the required future developments in the realm of human---robot collaboration.

/pdf/progress-and-prospects-of-the-human-robot-collaboration-58209jagph.pdf

Progress and prospects of the human---robot collaboration

http://www.centropiaggio.unipi.it/sites/default/files/softarm-MMT.pdf

An atlas of physical human-robot interaction

The extension of application domains of robotics from factories to human environments leads to implementing proper strategies for close interaction between people and robots. In order to avoid dangerous collision, force and vision based control can be used, while tracking human motion during such interaction.

Human-robot interaction control using force and vision

The most revolutionary and challenging feature of the next generation of robots will be physical Human– Robot Interaction (pHRI), where safety and dependability are the keys, also for paving the way to a successful introduction of robots into everyday human environments. In order to increase robot safety, all aspects of manipulator design, including mechanics, electronics, and software, should be considered. Among the possible strategies for achieving safety, one of the focuses is on strategy to prevent collisions. For unstructured domains, a detailed description of the environment is very difficult, if not impossible, to obtain. Therefore, reactive control can be used, in the presence of a good sensory system. Interaction control strategies as impedance control, together with reactive collision avoidance may increase safety by mean of control. Virtual reality can be used for realistic simulations of HRI task, including collisions and injected errors. Moreover, subjective comfort measures related to the use of a robotic manipulator can be accomplished, also related to the perceived safety during robot motion, depending on robot’s shape, speed and posture.

Safety issues for human-robot cooperation in manufacturing systems

The design of solutions for robotic extenders of wheelchairs must take into account both objective and subjective metrics for everyday activities in human environments. Virtual Reality (VR) constitutes a useful tool to effectively test design ideas and to verify performance criteria. This paper presents the development of a simulation environment, where three different manipulators to be mounted on a commercially available wheelchair have been considered. Experimental results are discussed in a significant case study, based upon users’ feedback.

Agostino De Santis

Papers

An atlas of physical human-robot interaction

Human-robot interaction control using force and vision

Safety issues for human-robot cooperation in manufacturing systems

A Virtual-Reality-based evaluation environment for wheelchair-mounted manipulators

Task-based Motion Control of Digital Humans for Industrial Applications