Building
computational
innovation to drive the physical intelligence of manufacturing/robotics:
•Developing
digital pipelines with
geometry computing and physical modelling;
•Design/fabricate customized functional systems.
•Connecting
digital/physical invention to social impact in healthcare and industry.
Multi-axis additive manufacturing (MAAM) has garnered significant attention for addressing the limitations of traditional planar-based 3D printing solutions, which rely on fixed nozzle orientations. By dynamically adjusting the local printing direction using high degrees-of-freedom setups - e.g., robot arm, MAAM enables advanced capabilities such as support-free printing, improved surface quality, and precise control over anisotropic material properties. Our research in MAAM focuses on the following key areas:
(1) Development of scalable computational tool for curved-based slicing and spatial toolpath generation.
(2) Collision-Free Motion Planning for multi-robot and redundant MAAM systems.
(3) Integration of functional materials with MAAM to fabricate multifunctional devices.
Selected Publication:
(p.s. projects listed here with clickable link are all open-source - see details in the project page.)
[1] INF-3DP: Implicit neural fields for collision-free multi-axis 3D printing, ACM Transactions on Graphics, 2025.
[2] Curve-based slicer for multi-axis DLP 3D printing, ACM Transactions on Graphics, 2025.
[3] Exceptional mechanical performance by spatial printing with continuous fiber, Additive Manufacturing, 2024.
[4] Learning Based Toolpath Planner on Diverse Graphs for 3D Printing, ACM Transactions on Graphics, 2023.
[5] S^3-Slicer: A general slicing framework for multi-axis 3D printing, ACM Transactions on Graphics, 2022. (Best paper award, SIG Asia 2022)
[6] Field-based toolpath generation for 3D printing continuous fibre reinforced thermoplastic composites, Additive Manufacturing, 2021.
[7] Reinforced FDM: Multi-axis filament alignment with controlled anisotropic strength, ACM Transactions on Graphics, 2020.
[8] Support-free volume printing by multi-axis motion, ACM Transactions on Graphics, 2018.
[9] RoboFDM: A robotic system for support-free fabrication using FDM, IEEE ICRA conference, 2017. (our research journey of MAAM starts here!)
Soft robots exhibit remarkable adaptability to their
surroundings and ensure safe human interaction, and the importance of
computational tools in soft robotics research is well-recognized, in our group, we encompassing the research in rapid prototyping (by 3D printing), kinematics computation (for configuration space), automated design, and sensor-based proprioception in soft robots.
Selected Publication:
(p.s. the numerical simulator in T-RO2020, IROS 2022, and T-OG 2024 are open-source)
[1] Correspondence-Free, Function-Based Sim-to-Real Learning for Deformable Surface Control, IEEE Transactions on Robotics, 2025.
[2] Accelerate neural subspace-based reduced-order solver of deformable simulation by Lipschitz optimization, ACM Transactions on Graphics, 2024.
[3] Spring-IMU fusion based proprioception for feedback control of soft manipulators, IEEE/ASME Transactions on Mechatronics , 2023.
[4] Efficient Jacobian-based inverse kinematics with sim-to-real transfer of soft robots by learning, IEEE/ASME Trans. Mechatronics, 2022.
[5] Soft robotic mannequin: design and algorithm for deformation control, IEEE/ASME Transactions on Mechatronics, 2022.
[6] Sensing and reconstruction of 3D deformation on pneumatic soft robots, IEEE/ASME Transactions on Mechatronics, 2021.
[7] Kinematics of soft robots by geometric computing, IEEE Transactions on Robotics, 2020, ICRA Conf. 2018.
IEEE IROS Conf. 2022.
