@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}
}
@article {544,
	title = {A Novel Parameter Estimation Method for Pneumatic Soft Hand Control Applying Logarithmic Decrement for Pseudo-Rigid Body Modeling},
	journal = {Advanced Intelligent Systems},
	year = {2024},
	pages = {2400637},
	author = {Zhang, Haiyun and Heung, Kelvin HoLam and Naquila, Gabrielle J and Hingwe, Ashwin and Deshpande, Ashish D}
}
@conference {526,
	title = {SCAPE: Learning Stiffness Control from Augmented Position Control Experiences},
	booktitle = {Proceedings of the 5th Conference on Robot Learning},
	year = {2022},
	month = {08{\textendash}11 Nov},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {<p>We introduce a sample-efficient method for learning state-dependent stiffness control policies for dexterous manipulation. The ability to control stiffness facilitates safe and reliable manipulation by providing compliance and robustness to uncertainties. Most current reinforcement learning approaches to achieve robotic manipulation have exclusively focused on position control, often due to the difficulty of learning high-dimensional stiffness control policies. This difficulty can be partially mitigated via policy guidance such as imitation learning. However, expert stiffness control demonstrations are often expensive or infeasible to record. Therefore, we present an approach to learn Stiffness Control from Augmented Position control Experiences (SCAPE) that bypasses this difficulty by transforming position control demonstrations into approximate, suboptimal stiffness control demonstrations. Then, the suboptimality of the augmented demonstrations is addressed by using complementary techniques that help the agent safely learn from both the demonstrations and reinforcement learning. By using simulation tools and experiments on a robotic testbed, we show that the proposed approach efficiently learns safe manipulation policies and outperforms learned position control policies and several other baseline learning algorithms.</p>
},
	url = {https://proceedings.mlr.press/v164/kim22b.html},
	author = {Kim, Mincheol and Niekum, Scott and Deshpande, Ashish D.},
	editor = {Faust, Aleksandra and Hsu, David and Neumann, Gerhard}
}
@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}
}
@article {448,
	title = {Improvement of Hand Functions of SCI Patients with EMG-driven Hand Exoskeleton: a Feasibility Study},
	journal = {Wearable Technologies},
	year = {2021},
	abstract = {<p>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.</p>
},
	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 {481,
	title = {SCAPE: Learning Stiffness Control from Augmented Position Control Experiences},
	booktitle = {Conference on Robot Learning. Proceedings of Machine Learning Research},
	year = {2021},
	author = {Mincheol Kim and Scott Niekum and Ashish Deshpande}
}
@conference {371,
	title = {Human-Inspired Object Manipulation Control with the Anatomically Correct  Testbed Hand},
	booktitle = {IEEE International Conference on Robotics and Automation},
	year = {2018},
	author = {Taylor D. Niehues and Ashish D. Deshpande}
}
@conference {318,
	title = {Development and validation of modeling framework for interconnected tendon networks in robotic and human fingers},
	booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Niehues, Taylor D and King, Raymond J and Deshpande, Ashish D and Keller, Sean}
}
@article {343,
	title = {Variable Thumb Moment Arm Modeling and Thumb-Tip Force Production of a Human-Like Robotic Hand},
	journal = {Journal of Biomechanical Engineering},
	volume = {139},
	year = {2017},
	pages = {101005},
	author = {Niehues, Taylor D and Deshpande, Ashish D}
}
@article {288,
	title = {A Simulation Framework for Virtual Prototyping of Robotic Exoskeletons},
	journal = {Journal of Biomechanical Engineering},
	volume = {138},
	year = {2016},
	author = {Agarwal, P. and Neptune, R. and Deshpande, A.D.}
}
@article {niehues2015compliance,
	title = {Compliance in parallel to actuators for improving stability of robotic hands during grasping and manipulation},
	journal = {The International Journal of Robotics Research},
	volume = {34},
	year = {2015},
	pages = {0278364914558016},
	publisher = {SAGE Publications},
	chapter = {256},
	author = {Niehues, Taylor D. and Rao, Prashant and Deshpande, A.D.}
}
@conference {317,
	title = {Effect of Parallel Compliance on Stability of Robotic Hands With Series Elastic Actuators},
	booktitle = {ASME Dynamic Systems and Controls Conference (DSCC)},
	year = {2015},
	month = {10/2015},
	author = {Prashant Rao and Taylor D. Niehues and Ashish D. Deshpande}
}
@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.}
}
@conference {Agarwal:ASB:2013,
	title = {Integration of Musculoskeletal Analysis with Engineering Design for Virtual Prototyping of Exoskeletons},
	booktitle = {American Society of Biomechanics},
	year = {2013},
	author = {Agarwal, Priyanshu and Kuo, Pei-Hsin and Neptune, Richard and Deshpande, Ashish D.}
}
@conference {agarwal2013novel,
	title = {A novel framework for virtual prototyping of rehabilitation exoskeletons},
	booktitle = {Rehabilitation Robotics (ICORR), 2013 IEEE International Conference on},
	year = {2013},
	pages = {1{\textendash}6},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Agarwal, Priyanshu and Kuo, Pei-Hsin and Neptune, Richard and Deshpande, Ashish D.}
}
