Website
Home
Research
Publications
Courses

Publications

Thesis

  1. Fan, Y. (2019). Dexterity in Robotic Grasping, Manipulation and Assembly online open access

Journals

  1. Fan, Y., & Tomizuka, M. (2019). Efficient grasp planning and execution with multifingered hands by surface fitting. IEEE Robotics and Automation Letters, 4(4), 3995-4002.

  2. Xinghao Zhu, Yefan Zhou, Yongxiang Fan, Lingfeng Sun, and Masayoshi Tomizuka “Learn to Grasp with Less Supervision: A Data-Efficient Posterior Grasp Sampling Loss” submitted to IEEE Robotics and Automation Letters.

Conference Proceedings

  1. Fan, Y., Zhu, X., & Tomizuka, M. (2019). Optimization Model for Planning Precision Grasps with Multi-Fingered Hands. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019, pp. 1548-1554, doi: 10.1109IROS40897.2019.8967560./

  2. Fan, Y., Luo, J., & Tomizuka, M. (2019). A Learning Framework for Precision Industrial Assembly. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 811-817). IEEE.

  3. Fan, Y., Lin, H.-C., Tang, T., & Tomizuka, M. (2019). A Learning Framework for Robust Bin Picking by Customized Grippers. Fan, Yongxiang, et al. “A learning framework for robust bin picking by customized grippers.” arXiv preprint arXiv:1809.08546 (2018).

  4. Fan, Y., Tang, T., Lin, H. C., & Tomizuka, M. (2018, October). Real-time grasp planning for multi-fingered hands by finger splitting. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4045-4052). IEEE.

  5. Fan, Y., Lin, H. C., Tang, T., & Tomizuka, M. (2018, August). Grasp planning for customized grippers by iterative surface fitting. In 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) (pp. 28-34). IEEE. (Best Application Paper Award)

  6. Fan, Y., Tang, T., Lin, H. C., Zhao, Y., & Tomizuka, M. (2017, September). Real-time robust finger gaits planning under object shape and dynamics uncertainties. In Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on (pp. 1267-1273). IEEE.

  7. Fan, Y., Sun, L., Zheng, M., Gao, W., & Tomizuka, M. (2017, July). Robust dexterous manipulation under object dynamics uncertainties. In Advanced Intelligent Mechatronics (AIM), 2017 IEEE International Conference on (pp. 613-619). IEEE. (Best Conference Paper Award Finalist)

  8. Fan, Y., Gao, W., Chen, W., & Tomizuka, M. (2017). Real-time finger gaits planning for dexterous manipulation. IFAC-PapersOnLine, 50(1), 12765-12772.

  9. Fan, Y., Lin, H. C., Zhao, Y., Lin, C. Y., Tang, T., Tomizuka, M., & Chen, W. (2016, August). Object position and orientation tracking for manipulators considering nonnegligible sensor physics. In Flexible Automation (ISFA), International Symposium on (pp. 450-457). IEEE.

  10. Tang, T., Fan, Y., Lin, H. C., & Tomizuka, M. (2017, September). State estimation for deformable objects by point registration and dynamic simulation. In Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on (pp. 2427-2433). IEEE.

  11. Lin, H. C., Fan, Y., Tang, T., & Tomizuka, M. (2016, October). Human guidance programming on a 6-DoF robot with collision avoidance. In Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on (pp. 2676-2681). IEEE.

  12. Xinghao Zhu, Yongxiang Fan, Shiyu Jin, Changhao Wang, and Masayoshi Tomizuka “Why Does Robotic Dexterous Hand Grasp Fail?” accepted by 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Workshop

  13. Lin, H. C., Tang, T., Fan, Y., Zhao, Y., Tomizuka, M., & Chen, W. (2016, June). Robot learning from human demonstration with remote lead hrough teaching. In Control Conference (ECC), 2016 European (pp. 388-394). IEEE.

  14. Lin, H. C., Liu, C., Fan, Y., & Tomizuka, M. (2017, August). Real-time collision avoidance algorithm on industrial manipulators. In Control Technology and Applications (CCTA), 2017 IEEE Conference on (pp. 1294-1299). IEEE.

  15. Lin, H. C., Tang, T., Fan, Y., & Tomizuka, M. (2018, October) A framework for robot grasping transferring with non-rigid transformation." In Intelligent Robots and Systems (IROS), 2018 IEEE/RSJ International Conference on.

  16. Xinghao Zhu, Lingfeng Sun, Yongxiang Fan, and Masayoshi Tomizuka “6-DoF Contrastive Grasp Proposal Network”, accepted by 2021 IEEE International Conference on Robotics and Automation (ICRA)

  17. Tang, T., Lin, H. C., Zhao, Y., Fan, Y., Chen, W., & Tomizuka, M. (2016, July). Teach industrial robots peg-hole-insertion by human demonstration. In Advanced Intelligent Mechatronics (AIM), 2016 IEEE International Conference on (pp. 488-494). IEEE.

Patents

Akeel, Hadi and Fan, Yongxiang. 2017. Vision guided robot path programming. U.S. Patent 10,556,347, granted on February 11, 2020 Yongxiang Fan. 2020. Network Modularization to Learn High Dimensional Robot Tasks. U.S. Utility Patent No.US61276-1 240531. Yongxiang Fan. 2020. Efficient Data Generation for Grasp Learning with General Grippers. U.S. Utility Patent No. US61419-1 242089 Yongxiang Fan. 2020. Grasp Learning Using Modularized Neural Networks. U.S. Utility Patent No. US61595-1 244930. Yongxiang Fan. 2021. Grasp Generation for Machine Tending. U.S. Utility Patent No. USxx247878]