Abstract
This column will try to describe the characteristics of current cyberpsychology research in Europe. In particular, CyberEurope aims at describing the leading research groups and projects running on the other side of the Ocean.
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During the last few decades, humans have increasingly relied on robots to perform a variety of tasks. Robots have been extensively used in production lines for tasks such as car assembly. However, despite their widespread success, automatic assembly still suffers from the time it takes to program and reprogram robots. To address this challenge, researchers have established the EU-funded project SARAFun. The project's new solution aims to empower industrial robots with perception learning and reasoning abilities, providing tools to automate robot program generation and design task-specific hardware.
Learning by Watching Human Behavior
“The system is built around the concept of a robot capable of learning and executing assembly tasks such as insertion or folding demonstrated by a human instructor,” explains Dr Ioannis Mariolis. After analysing the demonstration task, the robot generates and executes its own assembly program.
Based on the human instructor's feedback as well as sensory feedback from vision, force, and tactile sensors, the robot can progressively improve its performance in terms of speed and robustness. SARAFun's ambitious aim was to develop a system capable of executing new assembly tasks in less than a day. “Maintaining a human-like reach for assembly of small parts within a very small space is critical to minimizing the footprint on the factory floor. It also enables the robot to be installed into work stations currently used only by humans,” adds Dr Mariolis.
Implementation of the Interface
Work heavily relied on smooth and robust user interaction with the system using the human–robot interface. The latter consists of different modules enabling the operator to teach the assembly task to the robot and supervise the learning process. In the teaching phase, the user creates the assembly task by specifying a name, selecting the parts that the robot must assemble, and defining the type of assembly operation. In the next step, the assembly parts are placed within the camera view, and the system is called to identify them. After the parts have been detected, the operator demonstrates the assembly task in front of the camera. The recorded information is then analyzed, and important frames of the demonstration called “key frames” are automatically extracted. Upon user confirmation, the system uses the key frame information that includes the tracked positions of the assembly parts and the instructor's hands and generates the assembly program for the robot. Candidate grasps for the assembly parts are proposed, along with robotic finger designs for increasing grasp stability. After adjusting the grasping positions and designing appropriate fingers to compensate for the robot's inferior dexterity with respect to the human hand, the actual fingers are 3D printed and installed into the robot grippers. After loading the program for execution, the robot motions are generated using the information extracted from the key frames. The assembly operation keeps performing repeatedly until it reaches the desired level of autonomy.
Almost Zero Programming
Researchers have successfully completed many demonstrations using ABB’'s dual-arm collaborative robot YuMi (
Sources: Cordis, European Commission and European Union