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Learning-based Control for 4D Printing and Soft Robotics

Time: Fri 2022-11-11 09.00

Location: Gladan, Brinellvägen 85, Stockholm

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Language: English

Subject area: Production Engineering

Doctoral student: Qinglei Ji , Hållbara produktionssystem

Opponent: Professor Yong Chen, University of Southern California

Supervisor: Docent Lei Feng, Maskinkonstruktion (Avd.), Inbyggda styrsystem, KTH-centrum inom inbyggda system, ICES, Mekatronik, Maskinelement; Professor Lihui Wang, Hållbara produktionssystem; Docent Xi Vincent Wang, XPRES, Excellence in production research, Hållbara produktionssystem

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Exploiting novel sensors and actuators made of flexible and smart materials becomes a new trend in robotics research. The studies on the design, production, and control of the new type of robots motivate the research fields of soft robots and 4D printed robots. 3D Printing (3DP) is an additive manufacturing technology that is widely used in printing flexible materials to fabricate soft robots. 4D Printing (4DP) combines 3DP technologies with smart materials to produce transformable devices. 4DP first prints structures with specifically designed responsive materials. When external stimuli such as temperature, voltage, or magnetic field are applied to the printed structure, it changes shape in a programmable way. The shape morphing property of 4DP makes it a novel approach to the actuators of robots.

The employment of these special materials empowers these new robots with better compliance and adaptability to the working environment. However, compared with the rigid counterparts, they also have complex dynamic properties such as substantial non-linearity and time-variance. These factors make the precise modeling and robust control of these new robots challenging and thus hinder their potential applications. Focusing on soft robotic systems enabled by 3DP and 4DP approaches, this dissertation studies both traditional and Machine Learning (ML)-based approaches to the modeling, perception, and control of soft, non-linear, and time-variant robotic systems. The main contributions of this dissertation are:

  • The scheme of Closed-Loop (CL) controlled 4DP (CL4DP) using temperature stimulated Shape Memory Polymer (SMP) is designed and validated numerically and experimentally. The feedback control system increases the precision and robustness of the shape morphing process of 4D printed SMP. Applications of CL4DP are explored.
  • Data-driven model identification methods are applied to learn the dynamic model of the shape morphing process of CL4DP and the learned model has good quality to support model-based control design. Model-free and adaptive Reinforcement Learning (RL) controllers are developed to deal with the non-linearity and time variance of 4D printed actuators. To improve the stability and quick adaptability, a concise basis function set is selected instead of blindly using Deep Neural Networks (DNNs).
  • A quadruped robot enabled by soft actuators and its simulation model are developed. The computation efficiency and model accuracy of the simulator are studied and optimized by comparing different simulation methods such as Finite Element Method (FEM) and lumped parameter method.
  • The optimal walking gait pattern of a soft-legged quadruped robot is found by grid parameter search and RL with a physics based simulation model. To speed up the RL training process, modeling tricks are used to reduce the simulation time of the model and curriculum learning is used to reduce the learning time.
  • A soft sensor made by printable conductive materials and 3DP is designed and optimally calibrated to estimate the shape of a pneumatically driven soft actuator. The geometry of the soft sensor is optimally designed for the best linearity, hysteresis and drift properties. The online estimation is based on a linear regression model learned from experimental data.
  • A pneumatically driven soft gripper is developed by 3DP, the printable soft sensor, and pole-placement control methods. The operation of the gripper does not require an external image feedback system to measure its shape, which is estimated by the integrated soft sensor. The position feedback by the soft sensor and the controller by the pole-placement method enable the soft gripper to perform complex tasks with high precision.