Learning from Demonstration and Safe Cobotics Using Digital Twins

The use of collaborative robots, or cobots, is nowadays continually increasing, especially in the small- and medium-sized manufacturing sector. For each particular use case, the integration and deployment of a cobot into a collaborative workspace faces a certain number of challenges. Programming industrial robots, for example, can be a relatively complex and time-consuming task. In this paper we report an accurate method to robot programming by using an optimized “learning from demonstration” technique. The operator/programmer performs in real-time the corresponding task to be automatized, and by means of a tracker sensor the programmer’s motions are captured and transmitted to the robot; the robot registers the trajectories and is now able to reproduce the human movements with high accuracy. Another fundamental issue for cobot deployment is safety. In this paper, we also present a virtual/augmented reality (VR/AR) environment to facilitate the design and operation of cobots in order to maximize human safety. The virtual reality environment operates as an aide tool during the design phase. The human operator and the robot’s digital twin work side-by-side while executing a collaborative task in a virtual reality space. Their movements are controlled and registered, and after a given period of test time, the data is analyzed to suggest modifications to ensure a safe workspace (collision free) and to increase productivity. For the regular real-time cobot operation, an augmented reality environment was developed, again, with the purpose of assuring a safe human-robot collaboration. The augmented reality environment keeps tracking permanently the cobot and the human manipulations. This system produces audio and visual alarm signals in unsafe situations and is also able to take actions, such as slowing down or stopping the robot, to preserve the physical integrity of the human operator

Capability-based Frameworks for Industrial Robot Skills: a Survey

The research community is puzzled with words like skill, action, atomic unit and others when describing robots? capabilities. However, for giving the possibility to integrate capabilities in industrial scenarios, a standardization of these descriptions is necessary. This work uses a structured review approach to identify commonalities and differences in the research community of robots? skill frameworks. Through this method, 210 papers were analyzed and three main results were obtained. First, the vast majority of authors agree on a taxonomy based on task, skill and primitive. Second, the most investigated robots? capabilities are pick and place. Third, industrial oriented applications focus more on simple robots? capabilities with fixed parameters while ensuring safety aspects. Therefore, this work emphasizes that a taxonomy based on task, skill and primitives should be used by future works to align with existing literature. Moreover, further research is needed in the industrial domain for parametric robots? capabilities while ensuring safety

Robot Learning From Human Observation Using Deep Neural Networks

Industrial robots have gained traction in the last twenty years and have become an integral component in any sector empowering automation. Specifically, the automotive industry implements a wide range of industrial robots in a multitude of assembly lines worldwide. These robots perform tasks with the utmost level of repeatability and incomparable speed. It is that speed and consistency that has always made the robotic task an upgrade over the same task completed by a human. The cost savings is a great return on investment causing corporations to automate and deploy robotic solutions wherever feasible. The cost to commission and set up is the largest deterring factor in any decision regarding robotics and automation. Currently, robots are traditionally programmed by robotic technicians, and this function is carried out in a manual process in a well-structured environment. This thesis dives into the option of eliminating the programming and commissioning portion of the robotic integration. If the environment is dynamic and can undergo various iterations of parts, changes in lighting, and part placement in the cell, then the robot will struggle to function because it is not capable of adapting to these variables. If a couple of cameras can be introduced to help capture the operator’s motions and part variability, then Learning from Demonstration (LfD) can be implemented to potentially solve this prevalent issue in today’s automotive culture. With assistance from machine learning algorithms, deep neural networks, and transfer learning technology, LfD can strive and become a viable solution. This system was developed with a robotic cell that can learn from demonstration (LfD). The proposed approach is based on computer vision to observe human actions and deep learning to perceive the demonstrator’s actions and manipulated objects

Intuitive Instruction of Industrial Robots : A Knowledge-Based Approach

With more advanced manufacturing technologies, small and medium sized enterprises can compete with low-wage labor by providing customized and high quality products. For small production series, robotic systems can provide a cost-effective solution. However, for robots to be able to perform on par with human workers in manufacturing industries, they must become flexible and autonomous in their task execution and swift and easy to instruct. This will enable small businesses with short production series or highly customized products to use robot coworkers without consulting expert robot programmers. The objective of this thesis is to explore programming solutions that can reduce the programming effort of sensor-controlled robot tasks. The robot motions are expressed using constraints, and multiple of simple constrained motions can be combined into a robot skill. The skill can be stored in a knowledge base together with a semantic description, which enables reuse and reasoning. The main contributions of the thesis are 1) development of ontologies for knowledge about robot devices and skills, 2) a user interface that provides simple programming of dual-arm skills for non-experts and experts, 3) a programming interface for task descriptions in unstructured natural language in a user-specified vocabulary and 4) an implementation where low-level code is generated from the high-level descriptions. The resulting system greatly reduces the number of parameters exposed to the user, is simple to use for non-experts and reduces the programming time for experts by 80%. The representation is described on a semantic level, which means that the same skill can be used on different robot platforms. The research is presented in seven papers, the first describing the knowledge representation and the second the knowledge-based architecture that enables skill sharing between robots. The third paper presents the translation from high-level instructions to low-level code for force-controlled motions. The two following papers evaluate the simplified programming prototype for non-expert and expert users. The last two present how program statements are extracted from unstructured natural language descriptions

From Demonstrations to Skills for High-level Programming of Industrial Robots

In this paper we describe our approach to robotic skill representation and a prototypical implementation of a programming-by-demonstration approach that allows users to generate skills and robot program primitives for later re- finement and re-use. We intend to evaluate the applicability of this approach to high-level programming in a user study, which we also explain

Kinematics and Robot Design IV, KaRD2021

This volume collects the papers published on the special issue “Kinematics and Robot Design IV, KaRD2021” (https://www.mdpi.com/journal/robotics/special_issues/KaRD2021), which is the forth edition of the KaRD special-issue series, hosted by the open-access journal “MDPI Robotics”. KaRD series is an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2021, after the peer-review process, accepted 12 papers. The accepted papers cover some theoretical and many design/applicative aspects