Nicholas Adrian

Education

B.S. Nanyang Technological University (2018)

Award

Technology-Environment-Energy-Water-Society Fellowship (Fall 2024)

Research

I joined the lab in the Fall 2022 and has since been part of a collaborative research project to design, build, and control a highly dynamic humanoid robot. My initial work centered on developing a novel set of rigid-body dynamics algorithms capable of handling closed-chain sub-systems, which are increasingly prevalent in modern humanoids. Traditionally, existing algorithms face a trade-off between accuracy and computational efficiency. Our approach, however, seeks to achieve both high accuracy and low computational time.

A second line of research addresses whole-body manipulation in humanoid robots carrying heavy payloads. Here, I tackle the challenges posed by nonlinearities and high dimensionality using model-based optimization strategies. The proposed methods will ultimately be validated on a real humanoid platform for which I am building the mechatronic systems.

The third project explores improved inertial parameter estimation by leveraging higher-order polynomials to characterize these parameters. This approach has the potential to deliver more accurate results even in the presence of imperfect measurements.

Prior to graduate school, I received my B.S. in Mechanical Engineering from Nanyang Technological University, Singapore in 2018. Upon completing my undergraduate degree, I took up a full-time research position with Control Robotics Intelligence Group led by Assoc Prof Pham Quang Cuong. Most of my research work revolved around industrial robotics manipulation in unstructured environment, and are done in collaboration with HP Inc as part of the HP-NTU Digital Manufacturing Corporate Lab.

Publication

Journal

• Chignoli, M., Adrian, N., Kim, S. and Wensing, P.M., 2025. A propagation perspective on recursive forward dynamics for systems with kinematic loops. IEEE Transactions on Robotics.

Conference

• N. Fey, N. Adrian, P. M. Wensing, and M. Lemmon. A learning-based framework to adapt legged robots on-the-fly to unexpected disturbances. In 6th Annual Learning for Dynamics & Control Conference (accepted; to appear), 2024.
• Nguyen, Q. N., Adrian, N., & Pham, Q. C. (2023, May). Task-space clustering for mobile manipulator task sequencing. In 2023 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3693-3699). IEEE.
• Adrian, N., Do, V. T., & Pham, Q. C. (2022, August). DFBVS: Deep feature-based visual servo. In 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (pp. 1783-1789). IEEE.
• Nguyen, H., Adrian, N., Yan, J. L. X., Salfity, J. M., Allen, W., & Pham, Q. C. (2020, May). Development of a robotic system for automated decaking of 3D-printed parts. In 2020 IEEE International Conference on Robotics and Automation (ICRA)(pp. 8202-8208). IEEE.

Workshop

• M. Chignoli, N. Adrian, S. Kim, P. M. Wensing. Improving Contact-Rich Robotic Simulation with Generalized Rigid-Body Dynamics Algorithms. In ICRA 2023 Workshop: Embracing Contacts.
• Y. S. Lee, N. Vuong, N. Adrian, Q.-C. Pham. Integrating Force-based Manipulation Primitives with Deep Learning-based Visual Servoing for Robotic Assembly. In ICRA 2022 Workshop: Reinforcement Learning for Contact-Rich Manipulation.

Patent

• J. Salfity, W. Allen, H. Nguyen, N. Adrian , J. X. Y. Lim, Q.-C. Pham. 3D-printed object cleaning. US Patent 12,311,605. 2025.
• V.-T. Do, Q.-C. Pham, N. Adrian. Powder removal coverages. PCT App. PCT/US2022/046443

Life

I recently came across James A. Michener’s words on the art of living, and at this moment they are the only ones I can truly associate with the word "Life" - more as an aspiration than a description. Who knows what I will write here next year.