@article {IJRRLosey2021, title = {Physical interaction as communication: Learning robot objectives online from human corrections}, journal = {The International Journal of Robotics Research}, volume = {41}, number = {1}, year = {2022}, month = {Jan 2022}, pages = {02783649211050958}, chapter = {20-44}, abstract = {

When a robot performs a task next to a human, physical interaction is inevitable: the human might push, pull, twist, or guide the robot. The state of the art treats these interactions as disturbances that the robot should reject or avoid. At best, these robots respond safely while the human interacts; but after the human lets go, these robots simply return to their original behavior. We recognize that physical human{\textendash}robot interaction (pHRI) is often intentional: the human intervenes on purpose because the robot is not doing the task correctly. In this article, we argue that when pHRI is intentional it is also informative: the robot can leverage interactions to learn how it should complete the rest of its current task even after the person lets go. We formalize pHRI as a dynamical system, where the human has in mind an objective function they want the robot to optimize, but the robot does not get direct access to the parameters of this objective: they are internal to the human. Within our proposed framework human interactions become observations about the true objective. We introduce approximations to learn from and respond to pHRI in real-time. We recognize that not all human corrections are perfect: often users interact with the robot noisily, and so we improve the efficiency of robot learning from pHRI by reducing unintended learning. Finally, we conduct simulations and user studies on a robotic manipulator to compare our proposed approach with the state of the art. Our results indicate that learning from pHRI leads to better task performance and improved human satisfaction.

}, doi = {10.1177/02783649211050958}, url = {https://doi.org/10.1177/02783649211050958}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_IJRR2021.pdf}, author = {Dylan P. Losey and Andrea Bajcsy and Marcia K. O{\textquoteright}Malley and Anca D. Dragan} } @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} } @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 {1817, title = {Maintaining Subject Engagement during Robotic Rehabilitation with a Minimal Assist-as-Needed (mAAN) Controller}, year = {2017}, month = {07/2017}, publisher = {IEEE}, address = {London, UK}, abstract = {

One challenge of robotic rehabilitation interventions is devising ways to encourage and maintain high levels of subject involvement over long duration therapy sessions. Assist-as-needed controllers have been proposed which modulate robot intervention in movements based on measurements of subject involvement. This paper presents a minimal assist-as-needed controller, which modulates allowable error bounds and robot intervention based on sensorless force measurement accomplished through a nonlinear disturbance observer. While similar algorithms have been validated using healthy subjects, this paper presents a validation of the proposed mAAN control algorithm{\textquoteright}s ability to encourage user involvement with an impaired individual. User involvement is inferred from muscle activation, measured via surface electromyography (EMG). Experimental validation shows increased EMG muscle activation when using the proposed mAAN algorithm compared to non-adaptive algorithms.

}, issn = {978-1-5386-2296-4}, doi = {10.1109/ICORR.2017.8009222}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Pehlivan_2017_Maintaining.pdf}, author = {Ali Utku Pehlivan and Dylan P. Losey and Rose, Chad G.} } @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} }