End-point Impedance Measurements at Human Hand during Interactive Manual Welding with Robot

This paper presents a study of end-point impedance measurement at human hand, with professional and novice manual welders when they are performing Tungsten Inert Gas (TIG) welding interactively with the KUKA Light Weight Robot Arm (LWR). The welding torch is attached to the KUKA LWR, which is admittance controlled via a force sensor to give the feeling of a free floating mass at its end-effector. The subjects perform TIG welding on 1.5 mm thick stainless steel plates by manipulating the torch attached to the robot. The end-point impedance values are measured by introducing external force disturbances and by fitting a mass-damper-spring model to human hand reactions. Results show that, for professionals and novices, the mass, damping and stiffness values in the direction perpendicular to the welding line are the largest compared to the other two directions. The novices demonstrate less resistance to disturbances in this direction. Two of the professionals present larger stiffness and one of them presents larger damping. This study supports the hypothesis that impedance measurements could be used as a partial indicator, if not direct, of skill level to differentiate across different levels of manual welding performances. This work contributes towards identifying tacit knowledge of manual welding skills by means of impedance measurements

Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses

Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems

Automation of train cab front cleaning with a robot manipulator

In this letter we present a control and trajectory tracking approach for wiping the train cab front panels, using a velocity controlled robotic manipulator and a force/torque sensor attached to its end effector, without using any surface model or vision-based surface detection. The control strategy consists in a simultaneous position and force controller, adapted from the operational space formulation, that aligns the cleaning tool with the surface normal, maintaining a set-point normal force, while simultaneously moving along the surface. The trajectory tracking strategy consists in specifying and tracking a two dimensional path that, when projected onto the train surface, corresponds to the desired pattern of motion. We first validated our approach using the Baxter robot to wipe a highly curved surface with both a spiral and a raster scan motion patterns. Finally, we implemented the same approach in a scaled robot prototype, specifically designed by ourselves to wipe a 1/8 scaled version of a train cab front, using a raster scan pattern

Real-time 3D tracking of laparoscopy training instruments for assessment and feedback

Assessment of minimally invasive surgical skills is a non-trivial task, usually requiring the presence and time of expert observers, including subjectivity and requiring special and expensive equipment and software. Although there are virtual simulators that provide self-assessment features, they are limited as the trainee loses the immediate feedback from realistic physical interaction. The physical training boxes, on the other hand, preserve the immediate physical feedback, but lack the automated self-assessment facilities. This study develops an algorithm for real-time tracking of laparoscopy instruments in the video cues of a standard physical laparoscopy training box with a single fisheye camera. The developed visual tracking algorithm recovers the 3D positions of the laparoscopic instrument tips, to which simple colored tapes (markers) are attached. With such system, the extracted instrument trajectories can be digitally processed, and automated self-assessment feedback can be provided. In this way, both the physical interaction feedback would be preserved and the need for the observance of an expert would be overcome. Real-time instrument tracking with a suitable assessment criterion would constitute a significant step towards provision of real-time (immediate) feedback to correct trainee actions and show them how the action should be performed. This study is a step towards achieving this with a low cost, automated, and widely applicable laparoscopy training and assessment system using a standard physical training box equipped with a fisheye camera

Constraint-aware learning of policies by demonstration

[EN] Many practical tasks in robotic systems, such as cleaning windows, writing, or grasping, are inherently constrained. Learning policies subject to constraints is a challenging problem. In this paper, we propose a method of constraint-aware learning that solves the policy learning problem using redundant robots that execute a policy that is acting in the null space of a constraint. In particular, we are interested in generalizing learned null-space policies across constraints that were not known during the training. We split the combined problem of learning constraints and policies into two: first estimating the constraint, and then estimating a null-space policy using the remaining degrees of freedom. For a linear parametrization, we provide a closed-form solution of the problem. We also define a metric for comparing the similarity of estimated constraints, which is useful to pre-process the trajectories recorded in the demonstrations. We have validated our method by learning a wiping task from human demonstration on flat surfaces and reproducing it on an unknown curved surface using a force- or torque-based controller to achieve tool alignment. We show that, despite the differences between the training and validation scenarios, we learn a policy that still provides the desired wiping motion.The author(s) disclosed receipt of the following financial support for the research, auth/orship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy and the European Union (grant number DPI2016-81002-R (AEI/FEDER, UE)), the European Union Horizon 2020, as part of the project Memory of Motion - MEMMO (project ID 780684), and the Engineering and Physical Sciences Research Council, UK, as part of the Robotics and AI hub in Future AI and Robotics for Space - FAIR-SPACE (grant number EP/R026092/1), and as part of the Centre for Doctoral Training in Robotics and Autonomous Systems at Heriot-Watt University and the University of Edinburgh (grant numbers EP/L016834/1 and EP/J015040/1)Armesto, L.; Moura, J.; Ivan, V.; Erden, MS.; Sala, A.; Vijayakumar, S. (2018). 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