Electromyography-based Kinesthetic Teaching of Industrial Collaborative Robots

Current methods for robot teaching still lack intuitiveness and efficiency or require instrumentation of the environment (e.g., with cameras). This poses problems, especially for companies that cannot afford dedicated robot programmers. Classical online teaching with a teach pendant (TP) can be tedious and confusing, and kinesthetic teaching (KT) is often perceived as inefficient since toggling gravity compensation mode forces users to switch between interfaces or constrains them physically. We propose a novel robot teaching method that allows users to activate gravity compensation mode, confirm positions along trajectories, and manipulate the end effector. In our system, this is done via hand gestures %%that occur naturally while handling the robot and that we detect by equipping an operator with a wearable electromyography (EMG) armband. To evaluate our system, we compared it to a commercially available KT system in a user study that yielded statistical evidence that our approach is significantly faster while no difference regarding the perceived usability of the systems was found. Additionally, expert interviews confirm that the baseline system is state of the art and confirmed the market potential of EMG-based KT. Finally, we confirmed that the gesture classifier does not need to be re-trained for each user, which makes the proposed system a %%highly interesting option in practice that is cost-effective, efficient, and provides high teaching ergonomics.