Learning Human-Robot Collaboration Insights through the Integration of Muscular Activity in Interaction Motion Models [arxiv]
Longxin Chen, Shuangda Duan, Juan Rojas, and Yisheng Guan.

Abstract:

Recent progress in human-robot collaboration (HRC) makes fast and fluid interactions possible, even when human observations are partial and occluded. Methods like Interaction Probabilistic Movement Primitives (ProMPs) model human Cartesian trajectories through motion capture systems. However, such representation does not properly model tasks where similar motions are used to handle different objects. As such, under current approaches, a robot would not be able to properly adapt its pose and dynamics for proper handling. We propose to integrate the use of Electromyography (EMG) into the Interaction ProMP framework and utilize EMG-based muscular signals to augment the human observation representation. The contribution of our paper is the increased capacity to discern tasks that have similar trajectories but ones in which different tools are utilized and require the robot to adjust its pose for proper handling. Multidimensional Interaction ProMPs are used with an augmented vector that integrates muscle activity. Augmented time-normalized trajectories are used in training to learn correlation parameters and robot motions are predicted by finding a best weight combination and temporal scaling for a task. Collaborative single task scenarios with similar motions but different objects were used and compared. For one experiment only joint angles were recorded, for the other EMG signals were additionally integrated. Task recognition was computed for both tasks. Observation state vectors with augmented EMG signals were able to completely identify differences across tasks, while the baseline method failed every time. Integrating EMG signals into collaborative tasks significantly increases the ability of the system to recognize nuances in the tasks that are otherwise imperceptible, up to 74.6\% in our studies. Furthermore, the integration of EMG signals for collaboration also opens the door to a wide class of human-robot physical interactions based on haptic communication that have been largely unexploited in the field. Supplemental info including code, data, graphs, and result analysis can be found at [1].

Resources:

  • Video
    • Coming soon.

Copyright Notice:
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.