@article {Berning2021COBME, title = {Myoelectric Control and Neuromusculoskeletal Modeling: Complementary Technologies for Rehabilitation Robotics}, journal = {Current Opinion in Biomedical Engineering}, year = {2021}, pages = {100313}, abstract = {

Stroke and spinal cord injury (SCI) are a leading cause of disability in the United States, and researchers have pursued using robotic devices to aid rehabilitation efforts for resulting upper-extremity impairments. To date, however, robotic rehabilitation of the upper limb has produced only limited improvement in functional outcomes compared to traditional therapy. This paper explores the potential of myoelectric control and neuromusculoskeletal modeling for robotic rehabilitation using the current state of the art of each individual field as evidence. Continuing advances in the fields of myoelectric control and neuromusculoskeletal modeling offer opportunities for further improvements of rehabilitation robot control strategies. Specifically, personalized neuromusculoskeletal models driven by a subject{\textquoteright}s electromyography signals may provide accurate predictions of the subject{\textquoteright}s muscle forces and joint moments which, when used to design novel control strategies, could yield new approaches to robotic therapy for stroke and SCI that surpass the efficacy of traditional therapy.

}, keywords = {Electromyography, neuromusculoskeletal modeling, robotic rehabilitation, upper limb motor impairment}, issn = {2468-4511}, doi = {https://doi.org/10.1016/j.cobme.2021.100313}, url = {https://www.sciencedirect.com/science/article/pii/S2468451121000532}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/BerningCOBME2021_preprint.pdf}, author = {Jeffrey Berning and Gerard E. Francisco and Shuo-Hsiu Chang and Benjamin J. Fregly and Marcia K. O{\textquoteright}Malley} } @article {1969, title = {In the Fundamentals of Endovascular and Vascular Surgery model motion metrics reliably differentiate competency}, journal = {Journal of Vascular Surgery}, volume = {72}, number = {6}, year = {2020}, month = {12/2020}, pages = {2161-2165}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/JVS2020_Belvroy_et_al.pdf}, author = {Viony Belvroy and Barathwaj Murali and Malachi G. Sheahan and Marcia K. O{\textquoteright}Malley and Jean Bismuth} } @article {BHAGAT2020102502, title = {Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation}, journal = {NeuroImage: Clinical}, volume = {28}, year = {2020}, pages = {102502}, abstract = {

Brain-machine interfaces (BMI) based on scalp EEG have the potential to promote cortical plasticity following stroke, which has been shown to improve motor recovery outcomes. However, the efficacy of BMI enabled robotic training for upper-limb recovery is seldom quantified using clinical, EEG-based, and kinematics-based metrics. Further, a movement related neural correlate that can predict the extent of motor recovery still remains elusive, which impedes the clinical translation of BMI-based stroke rehabilitation. To address above knowledge gaps, 10 chronic stroke individuals with stable baseline clinical scores were recruited to participate in 12 therapy sessions involving a BMI enabled powered exoskeleton for elbow training. On average, 132\ {\textpm}\ 22 repetitions were performed per participant, per session. BMI accuracy across all sessions and subjects was 79\ {\textpm}\ 18\% with a false positives rate of 23\ {\textpm}\ 20\%. Post-training clinical assessments found that FMA for upper extremity and ARAT scores significantly improved over baseline by 3.92\ {\textpm}\ 3.73 and 5.35\ {\textpm}\ 4.62 points, respectively. Also, 80\% participants (7 with moderate-mild impairment, 1 with severe impairment) achieved minimal clinically important difference (MCID: FMA-UE \>5.2 or ARAT \>5.7) during the course of the study. Kinematic measures indicate that, on average, participants{\textquoteright} movements became faster and smoother. Moreover, modulations in movement related cortical potentials, an EEG-based neural correlate measured contralateral to the impaired arm, were significantly correlated with ARAT scores (ρ\ =\ 0.72, p\ \<\ 0.05) and marginally correlated with FMA-UE (ρ\ =\ 0.63, p\ =\ 0.051). This suggests higher activation of ipsi-lesional hemisphere post-intervention or inhibition of competing contra-lesional hemisphere, which may be evidence of neuroplasticity and cortical reorganization following BMI mediated rehabilitation therapy.

}, keywords = {Brain-machine interface, Clinical trial, Exoskeletons, Movement related cortical potentials, stroke rehabilitation}, issn = {2213-1582}, doi = {https://doi.org/10.1016/j.nicl.2020.102502}, url = {http://www.sciencedirect.com/science/article/pii/S2213158220303399}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/NeuroImage_2020_Bhagat_BMI_EEG_exo.pdf}, author = {Nikunj A. Bhagat and Nuray Yozbatiran and Jennifer L. Sullivan and Ruta Paranjape and Colin Losey and Zachary Hernandez and Zafer Keser and Robert Grossman and Gerard E. Francisco and Marcia K. O{\textquoteright}Malley and Jose L. Contreras-Vidal} } @proceedings {1990, title = {Towards Automated Performance Assessment using Velocity-based Motion Quality Metrics}, year = {2020}, month = {11/2020}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/ISMR_2020_Murali_et_al_FinalVersion_0.pdf}, author = {Barathwaj Murali and Viony Belvroy and Shivam Pandey and Michael D. Byrne and Jean Bismuth and Marcia K. O{\textquoteright}Malley} } @proceedings {1915, title = {A Bowden Cable-Based Series Elastic Actuation Module for Assessing the Human Wrist}, year = {2018}, month = {10/2018}, publisher = {ASME}, address = {Atlanta, GA}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/EwrinDSCC2018-8963.pdf}, author = {Andrew Erwin and Nick Moser and Craig. G. McDonald and Marcia K. O{\textquoteright}Malley} } @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} } @proceedings {1862, title = {Learning from Physical Human Corrections, One Feature at a Time}, year = {2018}, month = {03/2018}, publisher = {ACM/IEEE}, address = {Chicago, USA}, abstract = {

We focus on learning robot objective functions from human guidance: specifically, from physical corrections provided by the person while the robot is acting. Objective functions are typically parametrized in terms of features, which capture aspects of the task that might be important. When the person intervenes to correct the robot{\textquoteright}s behavior, the robot should update its understanding of which features matter, how much, and in what way. Unfortunately, real users do not provide optimal corrections that isolate exactly what the robot was doing wrong. Thus, when receiving a correction, it is difficult for the robot to determine which features the person meant to correct, and which features were changed unintentionally. In this paper, we propose to improve the efficiency of robot learning during physical interactions by reducing unintended learning. Our approach allows the human-robot team to focus on learning one feature at a time, unlike state-of-the-art techniques that update all features at once. We derive an online method for identifying the single feature which the human is trying to change during physical interaction, and experimentally compare this one-at-a-time approach to the all-at-once baseline in a user study. Our results suggest that users teaching one-at-a-time perform better, especially in tasks that require changing multiple features.

}, doi = {10.1145/3171221.3171267}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_HRI2018.pdf}, author = {Andrea Bajcsy and Dylan P. Losey and Marcia K. O{\textquoteright}Malley and Anca D. Dragan} } @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 {1853, title = {Trajectory deformations from physical human{\textendash}robot interaction}, journal = {IEEE Transactions on Robotics}, volume = {34}, number = {1}, year = {2018}, month = {02/2018}, pages = {126-138}, abstract = {

Robots are finding new applications where physical interaction with a human is necessary, such as manufacturing, healthcare, and social tasks. Accordingly, the field of physical human{\textendash}robot interaction (pHRI) has leveraged impedance control approaches, which support compliant interactions between human and robot. However, a limitation of traditional impedance control is that{\textemdash}despite provisions for the human to modify the robot{\textquoteright}s current trajectory{\textemdash}the human cannot affect the robot{\textquoteright}s future desired trajectory through pHRI. In this paper, we present an algorithm for physically interactive trajectory deformations which, when combined with impedance control, allows the human to modulate both the actual and desired trajectories of the robot. Unlike related works, our method explicitly deforms the future desired trajectory based on forces applied during pHRI, but does not require constant human guidance. We present our approach and verify that this method is compatible with traditional impedance control. Next, we use constrained optimization to derive the deformation shape. Finally, we describe an algorithm for real-time implementation, and perform simulations to test the arbitration parameters. Experimental results demonstrate reduction in the human{\textquoteright}s effort and improvement in the movement quality when compared to pHRI with impedance control alone.

}, doi = {10.1109/TRO.2017.2765335}, url = {http://ieeexplore.ieee.org/document/8115323/}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_TRO_2018.pdf}, author = {Dylan P. Losey and Marcia K. O{\textquoteright}Malley} } @article {1798, title = {Effects of assist-as-needed upper extremity robotic therapy after incomplete spinal cord injury: a parallel-group controlled trial}, journal = {Frontiers in Neurobotics}, volume = {11}, number = {26}, year = {2017}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/fnbot-11-00026.pdf}, author = {John M. Frullo and Jared Elinger and Ali Utku Pehlivan and Kyle Fitle and Kathryn Nedley and Gerard Francisco and Fabrizio Sergi and Marcia K. O{\textquoteright}Malley} } @proceedings {1821, title = {Effects of Discretization on the K-Width of Series Elastic Actuators}, year = {2017}, month = {05/2017}, pages = {421-426}, publisher = {IEEE}, address = {Singapore}, abstract = {

Rigid haptic devices enable humans to physically interact with virtual environments, and the range of impedances that can be safely rendered using these rigid devices is quantified by the Z-Width metric. Series elastic actuators (SEAs) similarly modulate the impedance felt by the human operator when interacting with a robotic device, and, in particular, the robot{\textquoteright}s perceived stiffness can be controlled by changing the elastic element{\textquoteright}s equilibrium position. In this paper, we explore the K-Width of SEAs, while specifically focusing on how discretization inherent in the computer-control architecture affects the system{\textquoteright}s passivity. We first propose a hybrid model for a single degree-of-freedom (DoF) SEA based on prior hybrid models for rigid haptic systems. Next, we derive a closed-form bound on the K-Width of SEAs that is a generalization of known constraints for both rigid haptic systems and continuous time SEA models. This bound is first derived under a continuous time approximation, and is then numerically supported with discrete time analysis. Finally, experimental results validate our finding that large pure masses are the most destabilizing operator in human-SEA interactions, and demonstrate the accuracy of our theoretical K-Width bound.

}, isbn = {978-1-5090-4633-1}, issn = {978-1-5090-4633-1}, doi = {10.1109/ICRA.2017.7989054}, url = {http://ieeexplore.ieee.org/abstract/document/7989054/}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_ICRA_2017.pdf}, author = {Dylan P. Losey and Marcia K. O{\textquoteright}Malley} } @proceedings {1830, title = {Learning Robot Objectives from Physical Human Interaction}, year = {2017}, month = {11/2017}, pages = {217-226}, publisher = {PMLR}, address = {Mountain View, CA}, abstract = {

When humans and robots work in close proximity, physical interaction is inevitable. Traditionally, robots treat physical interaction as a disturbance, and resume their original behavior after the interaction ends. In contrast, we argue that physical human interaction is informative: it is useful information about how the robot should be doing its task. We formalize learning from such interactions as a dynamical system in which the task objective has parameters that are part of the hidden state, and physical human interactions are observations about these parameters. We derive an online approximation of the robot{\textquoteright}s optimal policy in this system, and test it in a user study. The results suggest that learning from physical interaction leads to better robot task performance with less human effort.

}, keywords = {learning from demonstration, physical human-robot interaction}, url = {http://proceedings.mlr.press/v78/bajcsy17a.html}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/CoRL_2017.pdf}, author = {Andrea Bajcsy and Dylan P. Losey and Marcia K. O{\textquoteright}Malley and Anca D. Dragan} } @proceedings {1795, title = {The Rice Haptic Rocker: skin stretch haptic feedback with the Pisa/IIT SoftHand}, year = {2017}, month = {06/2017}, publisher = {IEEE}, address = {Munich, Germany}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/CameraReady.pdf}, author = {Edoardo Battaglia and Janelle P. Clark and Matteo Bianchi and Manuel G. Catalano and Antonio Bicchi and Marcia K. O{\textquoteright}Malley} } @proceedings {1892, title = {Simply Grasping Simple Shapes: Commanding a Humanoid Hand with a Shape-Based Synergy}, year = {2017}, month = {12/2017}, address = {Puerto Varas, Chile}, abstract = {

Despite rapid advancements in dexterity and mechanical design, the utility of humanoid robots outside of a controlled laboratory setting is limited in part due to the complexity involved in programming robots to grasp common objects. There exists a need for an efficient method to command high degree-of-freedom (DoF) position-controlled dexterous manipulators to grasp a range of objects such that explicit models are not needed for every interaction. The authors propose a method termed geometrical synergies that, similar to the neuroscience concept of postural synergies, aims to decrease the commanded DoF of the humanoid hand. In the geometrical synergy approach, the method relies on grasp design based on intuitive measurements of the object to be grasped, in contrast to postural synergy methods that focus on the principal components of human grasps to determine robot hand joint commands. For this paper, a synergy was designed to grasp cylinder-shaped objects. Using the SynGrasp toolbox, a model of a twelve-DoF hand was created to perform contact analysis around a small set of cylinders dened by a single variable, diameter. Experiments were performed with the robot to validate and update the synergy-based models. Successful manipulation of a large range of cylindrical objects not previously introduced to the robot was demonstrated. This geometric synergy-based grasp planning method can be applied to any position-controlled humanoid hand to decrease the number of commanded DoF based on simple, measureable inputs in order to grasp commonly shaped objects. This method has the potential to vastly expand the library of objects the robot can manipulate.

}, keywords = {Dexterous Hand, Grasp, Humanoid, Manipulation, Synergy}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/farrell2017simply.pdf}, author = {Logan C. Farrell and Troy A. Dennis and Julia A. Badger and Marcia K. O{\textquoteright}Malley} } @proceedings {1813, title = {A bio-inspired algorithm for identifying unknown kinematics from a discrete set of candidate models by using collision detection}, year = {2016}, pages = {418-423}, abstract = {

Many robots are composed of interchangeable modular components, each of which can be independently controlled, and collectively can be disassembled and reassembled into new configurations. When assembling these modules into an open kinematic chain, there are some discrete choices dictated by the module geometry; for example, the order in which the modules are placed, the axis of rotation of each module with respect to the previous module, and/or the overall shape of the assembled robot. Although it might be straightforward for a human user to provide this information, there is also a practical benefit in the robot autonomously identifying these unknown, discrete forward kinematics. To date, a variety of techniques have been proposed to identify unknown kinematics; however, these methods cannot be directly applied during situations where we seek to identify the correct model amid a discrete set of options. In this paper, we introduce a method specifically for finding discrete robot kinematics, which relies on collision detection, and is inspired by the biological concepts of body schema and evolutionary algorithms. Under the proposed method, the robot maintains a population of possible models, stochastically identifies a motion which best distinguishes those models, and then performs that motion while checking for a collision. Models which correctly predicted whether a collision would occur produce candidate models for the next iteration. Using this algorithm during simulations with a Baxter robot, we were able to correctly determine the order of the links in 84\% of trials while exploring around 0.01\% of all possible models, and we were able to correctly determine the axes of rotation in 94\% of trials while exploring \< 0.1\% of all possible models.

}, isbn = {978-1-5090-3287-7}, issn = {978-1-5090-3287-7}, doi = {10.1109/BIOROB.2016.7523663}, url = {http://ieeexplore.ieee.org/abstract/document/7523663/}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/BioRob_2016_Algorithm.pdf}, author = {Dylan P. Losey and C. G. McDonald and Marcia K. O{\textquoteright}Malley} } @proceedings {1822, title = {Improving the retention of motor skills after reward-based reinforcement by incorporating haptic guidance and error augmentation}, year = {2016}, pages = {857-863}, abstract = {

There has been significant research aimed at leveraging programmable robotic devices to provide haptic assistance or augmentation to a human user so that new motor skills can be trained efficiently and retained long after training has concluded. The success of these approaches has been varied, and retention of skill is typically not significantly better for groups exposed to these controllers during training. These findings point to a need to incorporate a more complete understanding of human motor learning principles when designing haptic interactions with the trainee. Reward-based reinforcement has been studied for its role in improving retention of skills. Haptic guidance, which assists a user to complete a task, and error augmentation, which exaggerates error in order to enhance feedback to the user, have been shown to be beneficial for training depending on the task difficulty, subject ability, and task type. In this paper, we combine the presentation of reward-based reinforcement with these robotic controllers to evaluate their impact on retention of motor skill in a visual rotation task with tunable difficulty using either fixed or moving targets. We found that with the reward-based feedback paradigm, both haptic guidance and error augmentation led to better retention of the desired visuomotor offset during a simple task, while during a more complex task, only subjects trained with haptic guidance demonstrated performance superior to those trained without a controller.

}, isbn = {978-1-5090-3287-7}, issn = {978-1-5090-3287-7}, doi = {10.1109/BIOROB.2016.7523735}, url = {http://ieeexplore.ieee.org/abstract/document/7523735/}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_BioRob_Improving.pdf}, author = {Dylan P. Losey and Laura H. Blumenschein and Marcia K. O{\textquoteright}Malley} } @article {1825, title = {Minimal assist-as-needed controller for upper limb robotic rehabilitation}, journal = {IEEE Transactions on Robotics}, volume = {32}, number = {1}, year = {2016}, month = {02/2016}, pages = {113-124}, chapter = {113}, abstract = {

Robotic rehabilitation of the upper limb following neurological injury is most successful when subjects are engaged in the rehabilitation protocol. Developing assistive control strategies that maximize subject participation is accordingly an active area of research, with aims to promote neural plasticity and, in turn, increase the potential for recovery of motor coordination. Unfortunately, state-of-the-art control strategies either ignore more complex subject capabilities or assume underlying patterns govern subject behavior and may therefore intervene suboptimally. In this paper, we present a minimal assist-as-needed (mAAN) controller for upper limb rehabilitation robots. The controller employs sensorless force estimation to dynamically determine subject inputs without any underlying assumptions as to the nature of subject capabilities and computes a corresponding assistance torque with adjustable ultimate bounds on position error. Our adaptive input estimation scheme is shown to yield fast, stable, and accurate measurements regardless of subject interaction and exceeds the performance of current approaches that estimate only position-dependent force inputs from the user. Two additional algorithms are introduced in this paper to further promote active participation of subjects with varying degrees of impairment. First, a bound modification algorithm is described, which alters allowable error. Second, a decayed disturbance rejection algorithm is presented, which encourages subjects who are capable of leading the reference trajectory. The mAAN controller and accompanying algorithms are demonstrated experimentally with healthy subjects in the RiceWrist-S exoskeleton.

}, issn = {1552-3098}, doi = {10.1109/TRO.2015.2503726}, url = {http://ieeexplore.ieee.org/abstract/document/7360218/}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/TRO_2016.pdf}, author = {Ali Utku Pehlivan and Dylan P. Losey and Marcia K. O{\textquoteright}Malley} } @proceedings {1826, title = {SOM and LVQ classification of endovascular surgeons using motion-based metrics}, year = {2016}, month = {01/2016}, pages = {227-237}, abstract = {

An increase in the prevalence of endovascular surgery requires a growing number of proficient surgeons. Current endovascular surgeon evaluation techniques are subjective and time-consuming; as a result, there is a demand for an objective and automated evaluation procedure. Leveraging reliable movement metrics and tool-tip data acquisition, we here use neural network techniques such as LVQs and SOMs to identify the mapping between surgeons{\textquoteright} motion data and imposed rating scales. Using LVQs, only 50\ \% testing accuracy was achieved. SOM visualization of this inadequate generalization, however, highlights limitations of the present rating scale and sheds light upon the differences between traditional skill groupings and neural network clusters. In particular, our SOM clustering both exhibits more truthful segmentation and demonstrates which metrics are most indicative of surgeon ability, providing an outline for more rigorous evaluation strategies.

}, issn = {978-3-319-28517-7}, doi = {https://doi.org/10.1007/978-3-319-28518-4_20}, url = {https://link.springer.com/chapter/10.1007/978-3-319-28518-4_20}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/WSOM_2016.pdf}, author = {Kramer, B.D. and Dylan P. Losey 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} } @article {1723, title = {Interaction control capabilities of an MR-compatible compliant actuator for wrist sensorimotor protocols during fMRI}, journal = {IEEE/ASME Transactions on Mechatronics}, volume = {20}, number = {6}, year = {2015}, pages = {2678-2690}, abstract = {

This paper describes the mechatronic design and characterization of a novel MR-compatible actuation system designed for a parallel force-feedback exoskeleton for measurement and/or assistance of wrist pointing movements during functional neuroimaging. The developed actuator is based on the interposition of custom compliant elements in series between a non-backdrivable MR-compatible ultrasonic piezoelectric motor and the actuator output. The inclusion of physical compliance allows estimation of interaction force, enabling force-feedback control and stable rendering of a wide range of haptic environments during continuous scanning. Through accurate inner-loop

velocity compensation and force-feedback control, the actuator is capable of displaying both a low-impedance, subject-in-charge mode, and a high stiffness mode. These modes enable the execution of shared haptic protocols during continuous fMRI.\ 

The detailed experimental characterization of the actuation system is presented, including a backdrivability analysis, demonstrating an achievable impedance range of 22 dB, within a bandwidth of 4 Hz (for low stiffness). The stiffness control bandwidth depends on the specific value of stiffness: a bandwidth of 4 Hz is achieved at low stiffness (10\% of the physical springs stiffness), while 8 Hz is demonstrated at higher stiffness. Moreover, coupled stability is demonstrated also for stiffness values substantially (25\%) higher than the physical stiffness of the spring. Finally, compatibility tests conducted in a 3T scanner are presented, validating the potential of inclusion of the actuator in an exoskeleton system for support of wrist movements during continuous MR scanning, without significant reduction in image quality.

}, keywords = {compliant actuators., Force control, functional MRI (fMRI), MR-compatible robotics}, doi = {10.1109/TMECH.2015.2389222}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/MR-compatible_actuator_v3.pdf}, author = {Fabrizio Sergi and Andrew Erwin and Marcia K. O{\textquoteright}Malley} } @proceedings {1743, title = {Compliant force-feedback actuation for accurate robot-mediated sensorimotor interaction protocols during fMRI}, year = {2014}, month = {08/2014}, publisher = {IEEE}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Sergi2014\%20-\%201DOF\%20MR\%20devices.pdf}, author = {Fabrizio Sergi and Andrew Erwin and Brian Cera and Marcia K. O{\textquoteright}Malley} } @article {1694, title = {Vary Slow Motion: Effect of Task Forces on Movement Variability and Implications for a Novel Skill Augmentation Mechanism}, journal = {IEEE Robotics and Automation Magazine}, year = {2014}, month = {08/2014}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Celik-O\%27Malley_IEEE-RAM_2014_press.pdf}, author = {Ozkan Celik and Marcia K. O{\textquoteright}Malley} } @proceedings {1650, title = {Design of a series elastic actuator for a compliant parallel wrist rehabilitation robot}, year = {2013}, month = {06/2013}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Sergi_SEA_paper_2.pdf}, author = {Fabrizio Sergi and Melissa M. Lee and Marcia K. O{\textquoteright}Malley} } @proceedings {1703, title = {Interaction control for rehabilitation robotics via a low-cost force sensing handle}, year = {2013}, address = {Palo Alto, CA}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Erwin2013\%20-\%20RiceWrist-Grip.pdf}, author = {Andrew Erwin and Fabrizio Sergi and Vinay Chawda and Marcia K. O{\textquoteright}Malley} } @proceedings {1702, title = {A Method for Selecting Velocity Filter Cutoff Frequency for Maximizing Impedance Width Performance in Haptic Interfaces}, year = {2013}, month = {10/2013}, address = {Palo Alto, CA}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Velocity\%20filtering_DSCC2013_final_version.pdf}, author = {Vinay Chawda and Ozkan Celik and Marcia K. O{\textquoteright}Malley} } @proceedings {1704, title = {Modeling Basic Aspects of Cyber-Physical Systems, Part II}, year = {2013}, address = {Tokyo, Japan}, abstract = {
We continue to consider the question of what
language features are needed to effectively model cyber-physical
systems (CPS). In previous work, we proposed using a core
language as a way to study this question, and showed how
several basic aspects of CPS can be modeled clearly in a
language with a small set of constructs. This paper reports
on the result of our analysis of two, more complex, case studies
from the domain of rigid body dynamics. The first one, a
quadcopter, illustrates that previously proposed core language
can support larger, more interesting systems than previously
shown. The second one, a serial robot, provides a concrete
example of why we should add language support for static
partial derivatives, namely that it would significantly improve
the way models of rigid body dynamics can be expressed.
}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/paper\%20\%285\%29.pdf}, author = {Yingfu Zeng and Rose, Chad G. and Paul Branner and Walid Taha and Jawad Masood and Roland Philippsen and Marcia K. O{\textquoteright}Malley and Robert Cartwright} } @proceedings {1684, title = {Reconstructing Surface EMG from Scalp EEG during Myoelectric Control of a Closed Looped Prosthetic Device}, year = {2013}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Paek\%20EMBC\%202013.pdf}, author = {Andrew Y. Paek and Jeremy D. Brown and R. B. Gillespie and Marcia K. O{\textquoteright}Malley and Patricia A. Shewokis and Jose L. Contreras-Vidal} } @proceedings {1685, title = {Understanding the Role of Haptic Feedback in a Teleoperated Grasp and Lift Task}, year = {2013}, month = {04/2013}, pages = {271-276}, abstract = {

Achieving dexterous volitional control of an upper-limb prosthetic device will require multimodal sensory feedback that goes beyond vision. Haptic display is well-positioned to provide this additional sensory information. Haptic display, however, includes a diverse set of modalities that encode information differently. We have begun to make a comparison between two of these modalities, force feedback spanning the elbow, and amplitude-modulated vibrotactile feedback, based on performance in a functional grasp and lift task. In randomly ordered trials, we assessed the performance of N=11 participants (8 able-bodied, 3 amputee) attempting to grasp and lift an object using an EMG controlled gripper under three feedback conditions (no feedback, vibrotactile feedback, and force feed-back), and two object weights that were undetectable by vision. Preliminary results indicate differences between able-bodied and amputee participants in coordination of grasp and lift forces. In addition, both force feedback and vibrotactile feedback contribute to significantly better task performance (fewer slips) and better adaptation following an unpredicted weight change. This suggests that the development and utilization of internal models for predictive control is more intuitive in the presence of haptic feedback.

}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/WH2013_FINAL_PRESS_Brown_et_al.pdf}, author = {Jeremy D. Brown and Andrew Paek and Mashaal Syed and Marcia K. O{\textquoteright}Malley and Patricia Shewokis and Jose L. Contreras-Vidal and R. B. Gillespie} } @proceedings {1686, title = {Vibrotactile Feedback of Pose Error Enhances Myoelectric Control of a Prosthetic Hand}, year = {2013}, month = {04/2013}, pages = {531-536}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/WH2013_FINAL_PRESS_Christiansen_et_al.pdf}, author = {Ryan Christiansen and Jose Luis Contreras-Vidal and R B Gillespie and Patricia Shewokis and Marcia K. O{\textquoteright}Malley} } @proceedings {1473, title = {On the Performance of Passivity-based Control of Haptic Displays Employing Levant{\textquoteright}s Differentiator for Velocity Estimation}, year = {2012}, month = {03/2012}, pages = {415-419}, publisher = {IEEE}, address = {Vancouver, BC, Canada}, abstract = {

In impedance-type haptic interfaces, encoders are typically employed to provide high resolution position measurements from which velocity is estimated, most commonly via the finite difference method (FDM). This velocity estimation technique performs reliably, unless very fast sampling is required, in which case noise or delay due to filtering of the position signals reduces accuracy in the estimate. Despite this limitation, FDM is attractive because it is a passive process, and therefore the passivity of the overall system can be guaranteed. Levant{\textquoteright}s differentiator is a viable alternative to FDM, and exhibits increased accuracy in velocity estimation at high sample rates compared to FDM. However, the passivity of this nonlinear velocity estimation technique cannot be shown using conventional methods. In this paper, we employ a time domain passivity framework to analyze and enforce passive behavior of Levant{\textquoteright}s differentiator for haptic displays in discrete time. The performance of this approach is explored both in simulation and experimentally on a custom made one degree-of-freedom haptic interface. Results demonstrate the effectiveness of the time domain passivity approach for compensating the active behavior observed with use of Levant{\textquoteright}s differentiator for velocity estimation.

}, isbn = {978-1-4673-0808-3}, doi = {10.1109/HAPTIC.2012.6183824}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/chawda.pdf}, author = {Vinay Chawda and Marcia K. O{\textquoteright}Malley} } @article {1563, title = {The RiceWrist Grip: A Means to Measure Grip Strength of Patients Using the RiceWrist}, year = {2012}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/grip_sensor_poster_mission_connect_0.pdf}, author = {Ryan Quincy and Andrew Erwin and A.U. Pehlivan and Yozbatiran, Nuray and Gerard Francisco and Marcia K. O{\textquoteright}Malley} } @article {powell_task, title = {The Task-Dependent Efficacy of Shared-Control Haptic Guidance Paradigms}, journal = {{IEEE} Transactions on Haptics}, volume = {5}, number = {3}, year = {2012}, pages = {208 {\textendash}219}, abstract = {

Shared-control haptic guidance is a common form of robot-mediated training used to teach novice subjects to perform dynamic tasks. Shared-control guidance is distinct from more traditional guidance controllers, such as virtual fixtures, in that it provides novices with real-time visual and haptic feedback from a real or virtual expert. Previous studies have shown varying levels of training efficacy using shared-control guidance paradigms; it is hypothesized that these mixed results are due to interactions between specific guidance implementations ( {amp;\#x201C;paradigms} {amp;\#x201D;)} and tasks. This work proposes a novel guidance paradigm taxonomy intended to help classify and compare the multitude of implementations in the literature, as well as a revised proxy rendering model to allow for the implementation of more complex guidance paradigms. The efficacies of four common paradigms are compared in a controlled study with 50 healthy subjects and two dynamic tasks. The results show that guidance paradigms must be matched to a task{\textquoteright}s dynamic characteristics to elicit effective training and low workload. Based on these results, we provide suggestions for the future development of improved haptic guidance paradigms.

}, issn = {1939-1412}, doi = {10.1109/TOH.2012.40}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/J8Powell2012.pdf}, author = {Powell, Dane and Marcia K. O{\textquoteright}Malley} } @proceedings {1114, title = {Application of Levant{\textquoteright}s Differentiator for Velocity Estimation and Increased Z-Width in Haptic Interfaces}, year = {2011}, month = {06/2011}, pages = {403-408}, publisher = {IEEE}, address = {Istanbul, Turkey}, issn = {978-1-4577-0297-6}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/1114-chawda.pdf}, author = {Vinay Chawda and Ozkan Celik and Marcia K. O{\textquoteright}Malley} } @proceedings {chawda2011, title = {A Lyapunov Approach for SOSM Based Velocity Estimation and its Application to Improve Bilateral Teleoperation Performance}, year = {2011}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/1322-DSCC2011-6181.pdf}, author = {Vinay Chawda and Marcia K. O{\textquoteright}Malley} } @article {1699, title = {Disturbance observer-based force estimation for haptic feedback}, journal = {ASME Journal of Dynamic Systems, Measurement and Control}, volume = {133}, year = {2010}, month = {12/2010}, pages = {014505-1--014505-4}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Gupta_O\%27Malley_2011_JDSMC_dist_obs_PRESS.pdf}, author = {Abhishek Gupta and Marcia K. O{\textquoteright}Malley} }