@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 {550, title = {Effect of exoskeleton-based weight support on upper extremities functional tasks}, journal = {Authorea Preprints}, year = {2024}, author = {Bucchieri, Anna and Khair, Alexander and Laffranchi, Matteo and Deshpande, Ashish} } @article {549, title = {Novel bio-inspired soft actuators for upper-limb exoskeletons: Design, fabrication and feasibility study}, journal = {Frontiers in Robotics and AI}, volume = {11}, year = {2024}, pages = {1451231}, author = {Zhang, Haiyun and Naquila, Gabrielle and Bae, Junghyun and Wu, Zonghuan and Hingwe, Ashwin and Deshpande, Ashish} } @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} } @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 {468, title = {An Anthropomorphic Passive Instrumented Hand for Validating Wearable Robotic Systems}, booktitle = {IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)}, year = {2021}, author = {Avinash Baskaran and Paria Esmatloo and Matthew Times and Ashish Deshpande and Chad G. Rose} } @conference {niehues2014cartesian, title = {Cartesian-space control and dextrous manipulation for multi-fingered tendon-driven hand}, booktitle = {Robotics and Automation (ICRA), 2014 IEEE International Conference on}, year = {2014}, pages = {6777{\textendash}6783}, publisher = {IEEE}, organization = {IEEE}, author = {Niehues, Taylor D. and Badger, Julia and Diftler, Myron and Deshpande, Ashish D.} } @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 {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 {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} }