@article {1859, title = {Closure to {\textquotedblleft}A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction"}, journal = {ASME Applied Mechanics Reviews}, volume = {70}, number = {1}, year = {2018}, month = {02/2018}, abstract = {

In their discussion article on our review paper, Professors James Schmiedeler and Patrick Wensing have provided an insightful and informative perspective of the roles of intent detection, arbitration, and communication as three pillars of a framework for the implementation of shared control in physical human{\textendash}robot interaction (pHRI). The authors both have significant expertise and experience in robotics, bipedal walking, and robotic rehabilitation. Their commentary introduces commonalities between the themes of the review paper and issues in locomotion with the aid of an exoskeleton or lower-limb prostheses, and presents several important topics that warrant further exploration. These include mechanical design as it pertains to the physical coupling between human and robot, modeling the human to improve intent detection and the arbitration of control, and finite-state machines as an approach for implementation. In this closure, we provide additional thoughts and discussion of these topics as they relate to pHRI.

}, doi = {10.1115/1.4039225}, url = {http://appliedmechanicsreviews.asmedigitalcollection.asme.org/article.aspx?articleID=2672398}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/amr_2018_closure.pdf}, author = {Dylan P. Losey and Craig G. McDonald and Edoardo Battaglia and Marcia K. O{\textquoteright}Malley} } @article {1858, title = {A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction}, journal = {ASME Applied Mechanics Reviews}, volume = {70}, number = {1}, year = {2018}, month = {02/2018}, abstract = {

As robotic devices are applied to problems beyond traditional manufacturing and industrial settings, we find that interaction between robots and humans, especially physical interaction, has become a fast developing field. Consider the application of robotics in healthcare, where we find telerobotic devices in the operating room facilitating dexterous surgical procedures, exoskeletons in the rehabilitation domain as walking aids and upper-limb movement assist devices, and even robotic limbs that are physically integrated with amputees who seek to restore their independence and mobility. In each of these scenarios, the physical coupling between human and robot, often termed physical human robot interaction (pHRI), facilitates new human performance capabilities and creates an opportunity to explore the sharing of task execution and control between humans and robots. In this review, we provide a unifying view of human and robot sharing task execution in scenarios where collaboration and cooperation between the two entities are necessary, and where the physical coupling of human and robot is a vital aspect. We define three key themes that emerge in these shared control scenarios, namely, intent detection, arbitration, and feedback. First, we explore methods for how the coupled pHRI system can detect what the human is trying to do, and how the physical coupling itself can be leveraged to detect intent. Second, once the human intent is known, we explore techniques for sharing and modulating control of the coupled system between robot and human operator. Finally, we survey methods for informing the human operator of the state of the coupled system, or the characteristics of the environment with which the pHRI system is interacting. At the conclusion of the survey, we present two case studies that exemplify shared control in pHRI systems, and specifically highlight the approaches used for the three key themes of intent detection, arbitration, and feedback for applications of upper limb robotic rehabilitation and haptic feedback from a robotic prosthesis for the upper limb.

}, doi = {DOI: 10.1115/1.4039145}, url = {http://appliedmechanicsreviews.asmedigitalcollection.asme.org/article.aspx?articleID=2671581}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/amr_2018_review.pdf}, author = {Dylan P. Losey and Craig G. McDonald and Edoardo Battaglia and Marcia K. O{\textquoteright}Malley} } @article {1829, title = {A Time-Domain Approach To Control Of Series Elastic Actuators: Adaptive Torque And Passivity-Based Impedance Control}, journal = {IEEE/ASME Transactions on Mechatronics}, volume = {21}, number = {4}, year = {2016}, pages = {2085 - 2096}, abstract = {

Robots are increasingly designed to physically interact with humans in unstructured environments, and as such must operate both accurately and safely. Leveraging compliant actuation, typically in the form of series elastic actuators (SEAs), can guarantee this required level of safety. To date, a number of frequency-domain techniques have been proposed which yield effective SEA torque and impedance control; however, these methods are accompanied by undesirable stability constraints. In this paper, we instead focus on a time-domain approach to the control of SEAs, and adapt two existing control techniques for SEA platforms. First, a model reference adaptive controller is developed, which requires no prior knowledge of system parameters and can specify desired closed-loop torque characteristics. Second, the time-domain passivity approach is modified to control desired impedances in a manner that temporarily allows the SEA to passively render impedances greater than the actuator{\textquoteright}s intrinsic stiffness. This approach also provides conditions for passivity when augmenting any stable SEA torque controller with an arbitrary impedance. The resultant techniques are experimentally validated on a custom prototype SEA.

}, issn = {1083-4435}, doi = {10.1109/TMECH.2016.2557727}, url = {http://ieeexplore.ieee.org/abstract/document/7457670/}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_TMECH.pdf}, author = {Dylan P. Losey and Andrew Erwin and Craig G. McDonald and Fabrizio Sergi and Marcia K. O{\textquoteright}Malley} }