TY - JOUR T1 - Fluidically programmed wearable haptic textiles JF - Device Y1 - 2023 A1 - Barclay Jumet A1 - Zane A. Zook A1 - Anas Yousaf A1 - Anoop Rajappan A1 - Doris Xu A1 - Te Faye Yap A1 - Nathaniel Fino A1 - Zhen Liu A1 - Marcia K. O’Malley A1 - Daniel J. Preston KW - analog control KW - fluidic control KW - haptic sleeve KW - human-machine interaction KW - human-robot interaction KW - Navigation KW - point force KW - smart textiles KW - spatiotemporal haptics KW - tactile cues AB -
Summary Haptic feedback offers a useful mode of communication in visually or auditorily noisy environments. The adoption of haptic devices in our everyday lives, however, remains limited, motivating research on haptic wearables constructed from materials that enable comfortable and lightweight form factors. Textiles, a material class fitting these needs and already ubiquitous in clothing, have begun to be used in haptics, but reliance on arrays of electromechanical controllers detracts from the benefits that textiles offer. Here, we mitigate the requirement for bulky hardware by developing a class of wearable haptic textiles capable of delivering high-resolution information on the basis of embedded fluidic programming. The designs of these haptic textiles enable tailorable amplitudinal, spatial, and temporal control. Combining these capabilities, we demonstrate wearables that deliver spatiotemporal cues in four directions with an average user accuracy of 87%. Subsequent demonstrations of washability, repairability, and utility for navigational tasks exemplify the capabilities of our approach.
UR - https://www.sciencedirect.com/science/article/pii/S2666998623000832 ER - TY - JOUR T1 - Physical interaction as communication: Learning robot objectives online from human corrections JF - The International Journal of Robotics Research Y1 - 2022 A1 - Dylan P. Losey A1 - Andrea Bajcsy A1 - Marcia K. O’Malley A1 - Anca D. Dragan AB -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–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.
VL - 41 UR - https://doi.org/10.1177/02783649211050958 ER - TY - Generic T1 - Toward training surgeons with motion-based feedback: Initial validation of smoothness as a measure of motor learning T2 - Human Factors and Ergonomics Society Annual Meeting Y1 - 2017 A1 - Shivam Pandey A1 - Michael D. Byrne A1 - William H. Jantscher A1 - Marcia K. O’Malley A1 - Priyanshu Agarwal AB -Surgery is a challenging domain for motor skill acquisition. A critical contributing factor in this difficulty is that feedback is often delayed from performance and qualitative in nature. Collection of highdensity motion information may offer a solution. Metrics derived from this motion capture, in particular indices of movement smoothness, have been shown to correlate with task outcomes in multiple domains, including endovascular surgery. The open question is whether providing feedback based on these metrics can be used to accelerate learning. In pursuit of that goal, we examined the relationship between a motion metric that is computationally simple to compute—spectral arc length—and performance on a simple but challenging motor task, mirror tracing. We were able to replicate previous results showing that movement smoothness measures are linked to overall performance, and now have performance thresholds to use in subsequent work on using these metrics for training.
JF - Human Factors and Ergonomics Society Annual Meeting VL - 61 UR - https://doi.org/10.1177/1541931213601747 ER - TY - JOUR T1 - Transcranial direct current stimulation (tDCS) of the primary motor cortex and robot-assisted arm training in chronic incomplete cervical spinal cord injury: A proof of concept sham-randomized clinical study JF - NeuroRehabilitation Y1 - 2016 A1 - Nuray Yozbatirana A1 - Zafer Keser A1 - Matthew Davis A1 - Argyrios Stampas A1 - Marcia K. O’Malley A1 - Catherine Cooper-Hay A1 - Joel Fronteraa A1 - Felipe Fregni A1 - Gerard E. Francisco VL - 39 ER - TY - Generic T1 - A Pre-Clinical Framework for Neural Control of a Therapeutic Upper-Limb Exoskeleton T2 - IEEE EMBS Conference on Neural Engineering Y1 - 2013 A1 - Amy Blank A1 - Marcia K. O’Malley A1 - Gerard E. Francisco A1 - Jose L. Contreras-Vidal JF - IEEE EMBS Conference on Neural Engineering ER -