@conference{Qiu2024DescribingRobots,

title = {Describing Robots from Design to Learning: Towards an Interactive Lifecycle Representation of Robots},

author = {Nuofan Qiu and Chaoyang Song and Fang Wan},

url = {https://github.com/bionicdl-sustech/ACDC4Robot

https://apps.autodesk.com/FUSION/en/Detail/Index?id=5028052292896011577},

doi = {10.1109/ICARM62033.2024.10715835},

year  = {2024},

date = {2024-06-08},

urldate = {2024-06-08},

booktitle = {IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2024)},

address = {Tokyo, Japan},

abstract = {The robot development process is divided into several stages, which create barriers to the exchange of information between these different stages. We advocate for an interactive lifecycle representation, extending from robot morphology design to learning, and introduce the role of robot description formats in facilitating information transfer throughout this pipeline. We analyzed the relationship between design and simulation, enabling us to employ robot process automation methods for transferring information from the design phase to the learning phase in simulation. As part of this effort, we have developed an open-source plugin called ACDC4Robot for Fusion 360, which automates this process and transforms Fusion 360 into a user-friendly graphical interface for creating and editing robot description formats. Additionally, we offer an out-of-the-box robot model library to streamline and reduce repetitive tasks. All codes are hosted open-source. (https://github.com/bionicdl-sustech/ACDC4Robot)},

keywords = {Authorship - Co-Author, Conf - ICARM},

pubstate = {published},

tppubtype = {conference}

}

@conference{Wu2024VisionBasedb,

title = {Vision-based, Low-cost, Soft Robotic Tongs for Shareable and Reproducible Tactile Learning},

author = {Tianyu Wu and Yujian Dong and Yang Xiao and Jinqi Wei and Fang Wan and Chaoyang Song},

url = {https://github.com/bionicdl-sustech/SoftRoboticTongs},

doi = {10.1109/ICARM62033.2024.10715842},

year  = {2024},

date = {2024-06-01},

urldate = {2024-06-01},

booktitle = {IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2024)},

address = {Tokyo, Japan},

abstract = {Recent research shows a growing interest in adopting touch interaction for robot learning, yet it remains challenging to efficiently acquire high-quality, structured tactile data at a low cost. In this study, we propose the design of vision-based soft robotic tongs to generate reproducible and shareable data of tactile interaction for learning. We further developed a web-based platform for convenient data collection and a portable assembly that can be deployed within minutes. We trained a simple network to infer the 6D force and torque using relative pose data from markers on the fingers and reached a reasonably high accuracy (an MAE of 0.548 N at 60 Hz within [0,20] N) but cost only 50 USD per set. The recorded tactile data is downloadable for robot learning. We further demonstrated the system for interacting with robotic arms in manipulation learning and remote control. We have open-sourced the system on GitHub with further information. (https://github.com/bionicdl-sustech/SoftRoboticTongs)},

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

pubstate = {published},

tppubtype = {conference}

}

@conference{Liu2023JigsawBased,

title = {Jigsaw-based Benchmarking for Learning Robotic Manipulation},

author = {Xiaobo Liu and Fang Wan and Sheng Ge and Haokun Wang and Haoran Sun and Chaoyang Song},

url = {http://www.ieee-arm.org/icarm2023/},

doi = {10.1109/ICARM58088.2023.10218784},

year  = {2023},

date = {2023-07-08},

urldate = {2023-07-08},

booktitle = {IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2023)},

address = {Sanya, China},

abstract = {Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence.},

keywords = {Authorship - Corresponding, Award - Best Conference Paper Finalist, Award - Paper, Conf - ICARM},

pubstate = {published},

tppubtype = {conference}

}