@conference {548, title = {BaRiFlex: A robotic gripper with versatility and collision robustness for robot learning}, booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2024}, publisher = {IEEE}, organization = {IEEE}, author = {Jeong, Gu-Cheol and Bahety, Arpit and Pedraza, Gabriel and Deshpande, Ashish D and Mart{\'\i}n-Mart{\'\i}n, Roberto} } @article {534, title = {Experimental and Simulation-Based Estimation of Interface Power During Physical Human-Robot Interaction in Hand Exoskeletons}, journal = {IEEE Robotics and Automation Letters}, volume = {9}, year = {2024}, month = {March}, pages = {2575-2581}, abstract = {

Even the best wearable robots face challenges with power losses in the system, especially at the physical attachment interface. While some sources for power loss are inherent to the system, such as human soft tissue or musculoskeletal joint damping, other sources such as soft padding materials and bias strap forces can be modulated to optimize interface power transmission. Few methods currently exist for estimating power loss at physical human-robot interfaces, especially for upper-body exoskeletons. This letter presents a novel method to estimate interface power from experimental data in a wearable hand device, along with a simulation model for predicting interaction behavior by incorporating viscoelastic properties at the attachment interface. The experimental method is implemented with the Maestro hand exoskeleton, and repeatability of the interface power estimation is confirmed with pilot human testing. Simulation results are compared with experimental estimation of interface power, showing agreement of trends and validating the use of a simulation model to predict physical human-robot interaction behavior. These findings highlight the advantages of multi-body simulations as a tool to perform modular, inexpensive, and predictive investigations in physical human-robot interaction, without affecting the real-world mechatronic system or hindering the subject{\textquoteright}s safety. The proposed tools for experimental estimation of interface power and simulation modeling can optimize the design and control of robots for seamless integration with the human body.

}, keywords = {design and human factors, Estimation, Exoskeletons, human-centered robotics, Physical human-robot interaction, Power measurement, Robots, Sea measurements, Torque, Torque measurement, wearable robotics}, issn = {2377-3766}, doi = {10.1109/LRA.2023.3326679}, author = {Yousaf, Saad N. and Mukherjee, Gaurav and King, Raymond and Deshpande, Ashish D.} } @booklet {536, title = {BaRiFlex: A Robotic Gripper with Versatility and Collision Robustness for Robot Learning}, year = {2023}, abstract = {

We present a new approach to robot hand design specifically suited for successfully implementing robot learning methods to accomplish tasks in daily human environments. We introduce BaRiFlex, an innovative gripper design that alleviates the issues caused by unexpected contact and collisions during robot learning, offering robustness, grasping versatility, task versatility, and simplicity to the learning processes. This achievement is enabled by the incorporation of low-inertia actuators, providing high Back-drivability, and the strategic combination of Rigid and Flexible materials which enhances versatility and the gripper{\textquoteright}s resilience against unpredicted collisions. Furthermore, the integration of flexible Fin-Ray linkages and rigid linkages allows the gripper to execute compliant grasping and precise pinching. We conducted rigorous performance tests to characterize the novel gripper{\textquoteright}s compliance, durability, grasping and task versatility, and precision. We also integrated the BaRiFlex with a 7 Degree of Freedom (DoF) Franka Emika{\textquoteright}s Panda robotic arm to evaluate its capacity to support a trial-and-error (reinforcement learning) training procedure. The results of our experimental study are then compared to those obtained using the original rigid Franka Hand and a reference Fin-Ray soft gripper, demonstrating the superior capabilities and advantages of our developed gripper system.

}, url = {https://arxiv.org/abs/2312.05323}, author = {Gu-Cheol Jeong and Arpit Bahety and Gabriel Pedraza and Ashish D. Deshpande and Roberto Mart{\'\i}n-Mart{\'\i}n} } @conference {533, title = {Characterizing the Onset and Offset of Motor Imagery During Passive Arm Movements Induced by an Upper-Body Exoskeleton}, booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023}, month = {Oct}, abstract = {

Two distinct technologies have gained attention lately due to their prospects for motor rehabilitation: robotics and brain-machine interfaces (BMIs). Harnessing their combined efforts is a largely uncharted and promising direction that has immense clinical potential. However, a significant challenge is whether motor intentions from the user can be accurately detected using non-invasive BMIs in the presence of instrumental noise and passive movements induced by the rehabilitation exoskeleton. As an alternative to the straight-forward continuous control approach, this study instead aims to characterize the onset and offset of motor imagery during passive arm movements induced by an upper-body exoskeleton to allow for the natural control (initiation and termination) of functional movements. Ten participants were recruited to perform kinesthetic motor imagery (MI) of the right arm while attached to the robot, simultaneously cued with LEDs indicating the initiation and termination of a goal-oriented reaching task. Using electroencephalogram signals, we built a decoder to detect the transition between i) rest and beginning MI and ii) maintaining and ending MI. Offline decoder evaluation achieved group average onset accuracy of 60.7\% and 66.6\% for offset accuracy, revealing that the start and stop of MI could be identified while attached to the robot. Furthermore, pseudo-online evaluation could replicate this performance, forecasting reliable online exoskeleton control in the future. Our approach showed that participants could produce quality and reliable sensorimotor rhythms regardless of noise or passive arm movements induced by wearing the exoskeleton, which opens new possibilities for BMI control of assistive devices.

}, keywords = {Decoding, Exoskeletons, Light emitting diodes, Manipulators, Neurorehabilitation, Performance evaluation, Robot sensing systems}, doi = {10.1109/IROS55552.2023.10342492}, author = {Mitra, Kanishka and Racz, Frigyes Samuel and Kumar, Satyam and Deshpande, Ashish D. and Del R. Mill{\'a}n, Jos{\'e}} } @article {530, title = {Kinematic coordinations capture learning during human-exoskeleton interaction}, journal = {Scientific Reports}, volume = {13}, year = {2023}, month = {06/2023}, abstract = {

Human{\textendash}exoskeleton interactions have the potential to bring about\ changes in human behavior for physical rehabilitation or skill augmentation. Despite significant advances in the design and control of these robots, their application to human training remains limited. The key obstacles to the design of such training paradigms are the prediction of human{\textendash}exoskeleton interaction effects and the selection of interaction control to affect human behavior. In this article, we present a method to elucidate behavioral changes in the human{\textendash}exoskeleton system and identify expert behaviors correlated with a task goal. Specifically, we observe the joint coordinations of the robot, also referred to as kinematic coordination behaviors, that emerge from human{\textendash}exoskeleton interaction during learning. We demonstrate the use of kinematic coordination behaviors with two task domains through a set of three human-subject studies. We find that participants (1) learn novel tasks within the exoskeleton environment, (2) demonstrate similarity of coordination during successful movements within participants, (3) learn to leverage these coordination behaviors to maximize success within participants, and (4) tend to converge to similar coordinations for a given task strategy across participants. At a high level, we identify task-specific joint coordinations that are used by different experts for a given task goal. These coordinations can be quantified by observing experts and the similarity to these coordinations can act as a measure of learning over the course of training for novices. The observed expert coordinations may further be used in the design of adaptive robot interactions aimed at teaching a participant the expert behaviors.

}, url = {https://www.nature.com/articles/s41598-023-35231-3}, author = {Keya Ghonasgi and Reuth Mirsky and Nisha Bhargava and Adrian M. Haith and Peter Stone and Ashish D. Deshpande} } @conference {532, title = {A Novel Control Law for Multi-Joint Human-Robot Interaction Tasks While Maintaining Postural Coordination}, booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023}, month = {Oct}, abstract = {

Exoskeleton robots are capable of safe torque-controlled interactions with a wearer while moving their limbs through predefined trajectories. However, affecting and assisting the wearer{\textquoteright}s movements while incorporating their inputs (effort and movements) effectively during an interaction re-mains an open problem due to the complex and variable nature of human motion. In this paper, we present a control algorithm that leverages task-specific movement behaviors to control robot torques during unstructured interactions by implementing a force field that imposes a desired joint angle coordination behavior. This control law, built by using principal component analysis (PCA), is implemented and tested with the Harmony exoskeleton. We show that the proposed control law is versatile enough to allow for the imposition of different coordination behaviors with varying levels of impedance stiffness. We also test the feasibility of our method for unstructured human-robot interaction. Specifically, we demonstrate that participants in a human-subject experiment are able to effectively perform reaching tasks while the exoskeleton imposes the desired joint coordination under different movement speeds and interaction modes. Survey results further suggest that the proposed control law may offer a reduction in cognitive or motor effort. This control law opens up the possibility of using the exoskeleton for training the participating in accomplishing complex multi-joint motor tasks while maintaining postural coordination.

}, keywords = {Behavioral sciences, Exoskeletons, Human-robot interaction, Robot kinematics, Surveys, Training, Trajectory}, doi = {10.1109/IROS55552.2023.10342501}, author = {Ghonasgi, Keya and Mirsky, Reuth and Haith, Adrian M. and Stone, Peter and Deshpande, Ashish D.} } @article {486, title = {Designing Physical Human-Robot Interaction Interfaces: A Scalable Method for Simulation Based Design}, journal = {Frontiers in Neurorobotics}, year = {2022}, author = {Varghese, R. J. and Mukherjee, G. and Deshpande, A. D.} } @conference {528, title = {Quantifying Changes in Kinematic Behavior of a Human-Exoskeleton Interactive System}, booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2022}, month = {Oct}, abstract = {

While human-robot interaction studies are becoming more common, quantification of the effects of repeated interaction with an exoskeleton remains unexplored. We draw upon existing literature in human skill assessment and present extrinsic and intrinsic performance metrics that quantify how the human-exoskeleton system{\textquoteright}s behavior changes over time. Specifically, in this paper, we present a new performance metric that provides insight into the system{\textquoteright}s kinematics associated with {\textquoteleft}successful{\textquoteright} movements resulting in a richer characterization of changes in the system{\textquoteright}s behavior. A human subject study is carried out wherein participants learn to play a challenging and dynamic reaching game over multiple attempts, while donning an upper-body exoskeleton. The results demonstrate that repeated practice results in learning over time as identified through the improvement of extrinsic performance. Changes in the newly developed kinematics-based measure further illumi-nate how the participant{\textquoteright}s intrinsic behavior is altered over the training period. Thus, we are able to quantify the changes in the human-exoskeleton system{\textquoteright}s behavior observed in relation with learning.

}, keywords = {Atmospheric measurements, Exoskeletons, Games, Human-robot interaction, Interactive systems, Kinematics, Training}, doi = {10.1109/IROS47612.2022.9981032}, author = {Ghonasgi, Keya and Mirsky, Reuth and Haith, Adrian M. and Stone, Peter and Deshpande, Ashish D.} } @conference {473, title = {Capturing Skill State in Curriculum Learning for Human Skill Acquisition}, booktitle = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)}, year = {2021}, author = {Keya Ghonasgi and Reuth Mirsky and Sanmit Narvekar and Bharath Masetty and Adrian Haith and Peter Stone and Ashish Deshpande} } @conference {469, title = {Is the Cerebellum a Model-Based Reinforcement Learning Agent?}, booktitle = {Adaptive and Learning Agents (ALA) Workshop at the International Conference on Autonomous Agents and Multiagent Systems}, year = {2021}, author = {Bharath Masetty and Reuth Mirsky and Ashish D. Deshpande and Michael Mauk and Peter Stone} } @article {448, title = {Improvement of Hand Functions of SCI Patients with EMG-driven Hand Exoskeleton: a Feasibility Study}, journal = {Wearable Technologies}, year = {2021}, abstract = {

We have developed a one-of-a-kind hand exoskeleton, called Maestro, which can power finger movements of those surviving severe disabilities to complete daily tasks using compliant joints. In this paper, we present results from a practical EMG control strategy conducted with spinal cord injury (SCI) patients (C5, C6 and C7) in which the subjects completed daily tasks controlling Maestro with electromyography (EMG) signals from their forearm muscles. With its compliant actuation and its degrees of freedom (DOF) that match the natural finger movements, Maestro is capable of helping the subjects grasp and manipulate a variety of daily objects (more than 15 from a standardized set). To generate control commands for Maestro, an artificial neural network algorithm was implemented along with a probabilistic control approach to classify and deliver four hand poses robustly with three EMG signals measured from the forearm and palm. Increase in the scores of a standardized test, called the Sollerman hand function test (SHFT), and enhancement in different aspects of grasping such as strength shows feasibility that Maestro can be capable of improving the hand function of SCI subjects.

}, author = {Youngmok Yun and Youngjin Na and Paria Esmatloo and Sarah Dancausse and Alfredo Serrato and Curtis A. Merring and Priyanshu Agarwal and Ashish D. Deshpande} } @conference {464, title = {Monitoring Fatigue-Induced Changes in Performance during Robot-Mediated Dynamic Movement}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, year = {2021}, author = {Kaci E. Madden and Dragan Djurdjanovic and Ashish D. Deshpande} } @article {454, title = {Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles}, journal = {Sensors}, year = {2021}, author = {Kaci Madden and Dragan Djurdjanovic and Ashish Deshpande} } @conference {378, title = {Designing Variable Stiffness Profiles To Optimize The Physical Human Robot Interface Of Hand Exoskeletons}, booktitle = {BioRob (IEEE International Conference on Biomedical Robotics and Biomechatronics)}, year = {2018}, author = {Varghese, R. J. and Mukherjee, G. and King, R. and Keller, S. and Deshpande, A. D.} } @conference {369, title = {Monitoring Human Neuromusculoskeletal System Performance during Spacesuit Glove Use: A Pilot Study}, booktitle = {IEEE Aerospace Conference}, year = {2018}, author = {Madden, Kaci E. and Djurdjanovic, D. and Deshpande, A.D.} } @article {300, title = {Design, Control and Testing of a Thumb Exoskeleton with Series Elastic Actuation}, journal = {International Journal of Robotics Research}, year = {2017}, keywords = {Exoskeleton, Hand, Thumb}, author = {Priyanshu Agarwal and Youngmok Yun and Jonas Fox and Kaci E. Madden and Ashish D. Deshpande} } @conference {319, title = {An EMG-Driven Assistive Hand Exoskeleton for Spinal Cord Injury Patients: Maestro}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, year = {2017}, author = {Youngmok Yun and Sarah Dancausse and Paria Esmatloo and Alfredo Serrato and Curtis A. Merring and Ashish D. Deshpande} } @conference {350, title = {The Influence of Robotic Assistance on Reducing Neuromuscular Effort and Fatigue during Extravehicular Activity Glove Use}, booktitle = {International Conference on Environmental Systems (ICES)}, year = {2017}, month = {07/2017}, author = {Madden, Kaci E. and Deshpande, Ashish D. and Peters, Benjamin J. and Rogers, Jonathan M. and Laske, Evan A. and McBryan, Emily R.} } @conference {334, title = {Methodologies for determining minimal grasping requirements and sensor locations for sEMG-based assistive hand orthosis for SCI patients}, booktitle = {IEEE International Conference of Rehabilitation Robotics (ICORR)}, year = {2017}, author = {Youngmok Yun and Paria Esmatloo and Alfredo Serrato and Curtis A. Merring and Ashish D. Deshpande} } @conference {289, title = {Accurate Torque Control of Finger Joints with UT Hand Exoskeleton through Bowden Cable SEA}, booktitle = {IEEE International Conference on Robotics and Automation (IROS)}, year = {2016}, author = {Youngmok Yun and Agarwal, P. and Fox, Jonas and Madden, K.E. and Deshpande, A.D.} } @conference {rose2015characterization, title = {Characterization of a hand-wrist exoskeleton, READAPT, via kinematic analysis of redundant pointing tasks}, booktitle = {Rehabilitation Robotics (ICORR), 2015 IEEE International Conference on}, year = {2015}, pages = {205{\textendash}210}, publisher = {IEEE}, organization = {IEEE}, author = {Rose, Chad G. and Sergi, Fabrizio and Yun, Youngmok and Madden, Kaci E. and Deshpande, A.D. and O{\textquoteright}Malley, Marcia K.} } @conference {315, title = {Crocheted Artificial Tendons and Ligaments for the Anatomically Correct Testbed (ACT) Hand}, booktitle = {IEEE ROBIO}, year = {2015}, author = {Z. Xu and Y. Matsuoka and Ashish D. Deshpande} } @article {madden2015integration, title = {On Integration of Additive Manufacturing During the Design and Development of a Rehabilitation Robot: A Case Study}, journal = {Journal of Mechanical Design}, year = {2015}, author = {Madden, Kaci E. and Deshpande, A.D.} } @inbook {deshpande2014development, title = {Development of an Anatomically Correct Testbed (ACT) Hand}, booktitle = {The Human Hand as an Inspiration for Robot Hand Development}, year = {2014}, pages = {453{\textendash}475}, publisher = {Springer}, organization = {Springer}, author = {Deshpande, Ashish D. and Matsuoka, Yoky} } @article {deshpande2013control, title = {Control strategies for the index finger of a tendon-driven hand}, journal = {The International Journal of Robotics Research}, volume = {32}, number = {1}, year = {2013}, pages = {115{\textendash}128}, publisher = {SAGE Publications}, author = {Deshpande, Ashish D. and Ko, Jonathan and Fox, Dieter and Matsuoka, Yoky} } @article {deshpande2013mechanisms, title = {Mechanisms of the anatomically correct testbed hand}, journal = {Mechatronics, IEEE/ASME Transactions on}, volume = {18}, number = {1}, year = {2013}, pages = {238{\textendash}250}, publisher = {IEEE}, author = {Deshpande, Ashish D. and Xu, Zhe and Weghe, Michael J Vande and Brown, Benjamin H and Ko, Jonathan and Chang, Lillian Y and Wilkinson, David D and Bidic, Sean M and Matsuoka, Yoky} } @article {deshpande2012contributions, title = {Contributions of intrinsic visco-elastic torques during planar index finger and wrist movements}, journal = {Biomedical Engineering, IEEE Transactions on}, volume = {59}, number = {2}, year = {2012}, pages = {586{\textendash}594}, publisher = {IEEE}, author = {Deshpande, Ashish D. and Gialias, Nick and Matsuoka, Yoky} } @article {deshpande2010acquiring, title = {Acquiring variable moment arms for index finger using a robotic testbed}, journal = {Biomedical Engineering, IEEE Transactions on}, volume = {57}, number = {8}, year = {2010}, pages = {2034{\textendash}2044}, publisher = {IEEE}, author = {Deshpande, Ashish D. and Balasubramanian, Ravi and Ko, Jonathan and Matsuoka, Yoky} } @conference {deshpande2009act, title = {ACT hand finger control: muscle and joint torque control strategies}, booktitle = {IEEE International Conference on Robotics and Automation}, year = {2009}, author = {Deshpande, Ashish D. and Ko, Jonathan and Fox, Dieter and Matsuoka, Yoky} } @conference {deshpande2008understanding, title = {Understanding variable moment arms for the index finger MCP joints through the ACT hand}, booktitle = {Biomedical Robotics and Biomechatronics, 2008. BioRob 2008. 2nd IEEE RAS \& EMBS International Conference on}, year = {2008}, pages = {776{\textendash}782}, publisher = {IEEE}, organization = {IEEE}, author = {Deshpande, Ashish D. and Balasubramanian, Ravi and Lin, Ralph and Dellon, Brian and Matsuoka, Yoky} }