@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} } @proceedings {1754, title = {Proportional sEMG based robotic assistance in an isolated wrist movement}, year = {2015}, month = {10/2015}, address = {Columbus, Ohio}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Atz2015\%20-\%20sEMG\%20based\%20robotic\%20assistance.pdf}, author = {Artz, E.J. and Blank, Amy A. and O{\textquoteright}Malley, M.K.} } @article {1718, title = {Position Synchronization in Bilateral Teleoperation Under Time-Varying Communication Delays}, year = {2014}, keywords = {adaptive control, Communication channels, Delay effects, delay systems, delays, Force, Force measurement, Ports (Computers), robust stability, Synchronization, telerobotics, time-varying systems}, doi = {10.1109/TMECH.2014.2317946}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/TMECH_Chawda2014_press.pdf}, author = {Chawda, V. and O{\textquoteright}Malley, M.K.} } @proceedings {1691, title = {A Pre-Clinical Framework for Neural Control of a Therapeutic Upper-Limb Exoskeleton}, year = {2013}, month = {2013}, pages = {1159-1162}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/BMI-EXO_2013_NER.pdf}, author = {Amy Blank and Marcia K. O{\textquoteright}Malley and Gerard E. Francisco and Jose L. Contreras-Vidal} } @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} } @proceedings {1881, title = {Preliminary Results in Virtual Testing for Smart Buildings}, year = {2012}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, abstract = {

Smart buildings promise to revolutionize the way we live. Applications ranging from climate control to fire management can have significant impact on the quality and cost of these services. However, a smart building and any technology with direct effect on the safety of its occupants must undergo extensive testing. Virtual testing by means of computer simulation can significantly reduce the cost of testing and, as a result, accelerate the development of novel applications. Unfortunately, building physically-accurate simulation codes can be labor intensive.

}, isbn = {978-3-642-29154-8}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/bruneau2012lnicst.pdf}, author = {Bruneau, Julien and Consel, Charles and O{\textquoteright}Malley, Marcia and Taha, Walid and Hannourah, Wail Masry}, editor = {S{\'e}nac, Patrick and Ott, Max and Seneviratne, Aruna} } @proceedings {1877, title = {A Preliminary ACT-R model of a continuous motor task}, year = {2010}, publisher = {SAGE Publications Sage CA: Los Angeles, CA}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/byme2010hfes.pdf}, author = {Byrne, Michael D and O{\textquoteright}Malley, Marcia K and Gallagher, Melissa A and Purkayastha, Sagar N and Howie, Nicole and Huegel, Joel C} } @proceedings {1746, title = {Progressive haptic and visual guidance for training in a virtual dynamic task}, year = {2010}, month = {March}, keywords = {Design engineering, dynamic motor skill, Error correction, expertise-based performance measures, Feedback, Fixtures, Haptic interfaces, haptic virtual environment, input frequency, Mechatronics, Performance analysis, progressive guidance controller, progressive haptic guidance, progressive visual guidance, Protocols, Rehabilitation robotics, skill component measures, target-hitting task, training protocol, trajectory error, virtual dynamic task, virtual environment, Virtual reality, visual guidance scheme}, doi = {10.1109/HAPTIC.2010.5444632}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Huegel2010HS.pdf}, author = {Huegel, J.C. and O{\textquoteright}Malley, M.K.} } @article {102, title = {Passive and Active Discrimination of Natural Frequency of Virtual Dynamic System}, journal = {IEEE Transactions on Haptics}, volume = {2}, number = {1}, year = {2009}, month = {02/2009}, pages = {40-51}, doi = {10.1109/TOH.2008.21}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/102-Israr-Li-Patoglu-O\%27Malley_IEEEToH_2\%281\%29_2009FINAL.pdf}, author = {Ali Israr and Yanfang Li and Volkan Patoglu and O{\textquoteright}Malley, M.K.} } @proceedings {109, title = {Progressive shared control for training in virtual environments}, year = {2009}, month = {03/2009}, pages = {332-337}, publisher = {IEEE}, address = {Salt Lake City, UT, USA}, keywords = {Haptic interface, performance, shared control, training}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/109-LiPSC-WHC.pdf}, author = {Yanfang Li and Joel C. Huegel and Volkan Patoglu and O{\textquoteright}Malley, M.K.} } @proceedings {9872945, title = {Passive and active kinesthetic perception just-noticeable-difference for natural frequency of virtual dynamic systems}, year = {2008}, note = {

active kinesthetic perception;passive kinesthetic perception;just-noticeable-difference;virtual second order dynamic systems;degree-of-freedom haptic device;haptic sensory feedback;virtual resonance task;visual feedback;

}, month = {03/2008}, pages = {25 - 31}, publisher = {IEEE}, address = {Reno, NE, USA}, abstract = {

This paper investigates the just-noticeable-difference (JND) for natural frequency of virtual second order dynamic systems. Using a one degree-of-freedom haptic device, visual and/or haptic sensory feedback were presented during interactions with the system. Participants were instructed to either perceive passively or actively excite the system in order to discriminate natural frequencies. The JND for this virtual resonance task ranged from 3.99 \% to 6.96 \% for reference frequencies of 1 Hz and 2 Hz. Results show that sensory feedback has a significant effect on JND in passive perception, with combined visual and haptic feedback enabling the best discrimination performance. In active perception, there is no significant difference on JND with haptic and combined visual and haptic feedback. There is also no significant difference between active perception and passive perception for this JND experiment. The presentation of systems with equivalent natural frequencies but different spring stiffness resulted in no large bias toward larger stiffness and no significant difference in JND for equivalent systems. This finding indicates that human participants do not discriminate natural frequency based on the maximum force magnitude perceived, as indicated by prior studies.

}, keywords = {Haptic interfaces, visual perception}, issn = {978-1-4244-2005-6}, doi = {10.1109/HAPTICS.2008.4479908}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4479908}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/58-HapticSymposium2008_Li.pdf}, author = {Yanfang Li and Ali Israr and Volkan Patoglu and O{\textquoteright}Malley, M.K.} } @inbook {104, title = {Principles of human-machine interfaces and interactions}, booktitle = {Life Science Automation: Fundamentals and Applications}, year = {2007}, pages = {101-125}, publisher = {Artech House Publishers}, organization = {Artech House Publishers}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/omalley2007lifescienceauto.pdf}, author = {O{\textquoteright}Malley, M.K.} } @proceedings {sledd_perf_2006, title = {Performance Enhancement of a Haptic Arm Exoskeleton}, year = {2006}, pages = {375{\textemdash}381}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/68-SleddOMalleyHAPTICS06final.pdf}, author = {Alan Sledd and O{\textquoteright}Malley, M.K.} } @proceedings {7780496, title = {Passive and active assistance for human performance of a simulated underactuated dynamic task}, year = {2003}, note = {

simulated underactuated dynamic task;machine-mediated training;virtual fixtures;active assist;haptic feedback;visual feedback;

}, pages = {348 - 55}, address = {Los Angeles, CA, USA}, abstract = {

Machine-mediated training of dynamic task completion is typically implemented with passive intervention via virtual fixtures or active assist by means of record and replay strategies. During interaction with a real dynamic system however, the user relies on both visual and haptic feedback real-time in order to elicit desired motions. This work investigates human performance in a Fitts{\textquoteright} type targeting task with an underactuated dynamic system. Performance, in terms of number of hits and between-target tap times, is measured while various passive and active control modes are displayed concurrently with the haptic feedback from the simulated system{\textquoteright}s own dynamic behavior. It Is hypothesized that passive and active assist modes that are implemented during manipulation of simulated underactuated systems could be beneficial in rehabilitation applications. Results indicate that human performance can be improved significantly with the passive and active assist modes

}, keywords = {Haptic interfaces, Virtual reality}, url = {http://dx.doi.org/10.1109/HAPTIC.2003.1191308}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/omalley2003HAPTICS.pdf}, author = {O{\textquoteright}Malley, M.K. and Abhishek Gupta} }