@conference{Guo2021VolumetricallyEnhanced,

title = {Volumetrically Enhanced Soft Actuator with Proprioceptive Sensing},

author = {Weijie Guo and Baiyue Wang and Shihao Feng and Hongdong Yi and Fang Wan and Chaoyang Song},

doi = {10.1109/LRA.2021.3072859},

year  = {2021},

date = {2021-04-12},

urldate = {2021-04-12},

booktitle = {IEEE International Conference on Soft Robotics (RoboSoft2021)},

address = {New Haven, CT, USA},

abstract = {Soft robots often show a superior power-to-weight ratio using highly compliant, light-weight material, which leverages various bio-inspired body designs to generate desirable deformations for life-like motions. In this letter, given that most material used for soft robots is light-weight in general, we propose a volumetrically enhanced design strategy for soft robots, providing a novel design guideline to govern the form factor of soft robots. We present the design, modeling, and optimization of a volumetrically enhanced soft actuator (VESA) with linear and rotary motions, respectively, achieving superior force and torque output, linear and rotary displacement, and overall extension ratio per unit volume. We further explored VESA's proprioceptive sensing capability by validating the output force and torque through analytical modeling and experimental verification. Our results show that the volumetric metrics hold the potential to be used as a practical design guideline to optimize soft robots’ engineering performance.},

note = {Dual-track Submission with RAL: https://doi.org/10.1109/LRA.2021.3072859},

keywords = {Authorship - Corresponding, Conf - RoboSoft, Special - Dual-Track},

pubstate = {published},

tppubtype = {conference}

}

@conference{Jiang2021LobsterInspired,

title = {Lobster-inspired Finger Surface Design for Grasping with Enhanced Robustness},

author = {Haiyang Jiang and Yonglin Jing and Ning Guo and Weijie Guo and Fang Wan and Chaoyang Song},

doi = {10.1109/RoboSoft51838.2021.9479215},

year  = {2021},

date = {2021-04-12},

urldate = {2021-04-12},

booktitle = {IEEE International Conference on Soft Robotics (RoboSoft2021)},

address = {New Haven, CT, USA},

keywords = {Authorship - Corresponding, Conf - RoboSoft},

pubstate = {published},

tppubtype = {conference}

}

@conference{Wan2020ReconfigurableDesign,

title = {A Reconfigurable Design for Omni-Adaptive Grasp Learning},

author = {Fang Wan and Haokun Wang and Jiyuan Wu and Yujia Liu and Sheng Ge and Chaoyang Song},

doi = {10.1109/LRA.2020.2982059},

year  = {2020},

date = {2020-05-15},

urldate = {2020-05-15},

booktitle = {IEEE International Conference on Soft Robotics (RoboSoft2020)},

address = {New Haven, CT, USA},

abstract = {The engineering design of robotic grippers presents an ample design space for optimization towards robust grasping. In this letter, we investigate how learning method can be used to support the design reconfiguration of robotic grippers for grasping using a novel soft structure with omni-directional adaptation. We propose a gripper system that is reconfigurable in terms of the number and arrangement of the proposed finger, which generates a large number of possible design configurations. Such design reconfigurations with omni-adaptive fingers enables us to systematically investigate the optimal arrangement of the fingers towards robust grasping. Furthermore, we adopt a learning-based method as the baseline to benchmark the effectiveness of each design configuration. As a result, we found that the 3-finger radial configuration is suitable for space-saving and cost-effectiveness, achieving an average 96% grasp success rate on seen and novel objects selected from the YCB dataset. The 4-finger radial arrangement can be applied to cases that require a higher payload with even distribution. We achieved dimension reduction using the radial gripper design with the removal of z-axis rotation during grasping. We also reported the different outcomes with or without friction enhancement of the soft finger network.},

note = {Dual-track Submission with RAL: https://doi.org/10.1109/lra.2020.2982059},

keywords = {Authorship - Corresponding, Conf - RoboSoft, Special - Dual-Track},

pubstate = {published},

tppubtype = {conference}

}

@conference{Wu2020RoboticCane,

title = {Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance},

author = {Xia Wu and Haiyuan Liu and Ziqi Liu and Mingdong Chen and Fang Wan and Chenglong Fu and Harry Asada and Zheng Wang and Chaoyang Song},

doi = {10.1109/robosoft48309.2020.9116028},

year  = {2020},

date = {2020-05-15},

urldate = {2020-05-15},

booktitle = {IEEE International Conference on Soft Robotics (RoboSoft2020)},

address = {New Haven, CT, USA},

keywords = {Authorship - Corresponding, Conf - RoboSoft},

pubstate = {published},

tppubtype = {conference}

}

@conference{Yang2020ScalableTactile,

title = {Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger},

author = {Zeyi Yang and Sheng Ge and Fang Wan and Yujia Liu and Chaoyang Song},

doi = {10.1109/robosoft48309.2020.9116026},

year  = {2020},

date = {2020-05-15},

urldate = {2020-05-15},

booktitle = {IEEE International Conference on Soft Robotics (RoboSoft2020)},

address = {New Haven, CT, USA},

keywords = {Authorship - Corresponding, Conf - RoboSoft},

pubstate = {published},

tppubtype = {conference}

}