Toward Soft Robotics for Precise Manipulation

Interdisciplinary Areas: Data and Engineering Applications, Autonomous and Connected Systems

Project Description

The research objective of this project is to explore soft robotic manipulators that are inherently safe to physically interact with the environment and simultaneously have the capability to accurately perform challenging robotic manipulation tasks, like peg-in-hole insertion and screw assembly at home/workplace, as well as pick and place tasks in agriculture. The research activities include the design and manufacturing of soft robotic manipulators, the development of high-precision and high-dimension proprioception systems, and the implementation of optimal control and trajectory planning to complete the manipulation tasks. Soft robots, not like the traditional industrial robots, are inherently safe for human operators and delicate objects, and they are ideal to share the workspace with humans and work with humans side by side to help people do repetitive, dirty, and unsafe tasks. However, it is very challenging to precisely control soft robots because they have infinite degrees of freedom (DOFs) and have very complex deformation. It is very difficult to obtain the accurate proprioception feedback of the soft robots. This project proposes a potential solution to develop inherently safe and high-performing robotic systems that can work with humans side by side to accomplish challenging tasks. 

Start Date

Spring/Fall 2022

Postdoctoral Qualifications

 Solid backgrounds in soft robotics, control, machine learning, perception, and manipulation;
• Passion and interest to solve challenging research problems using methodologies from different areas;
• Good communication and writing skills;
• Ability to thrive in a collaborative environment. 


Yu She
Assistant Professor
School of Industrial Engineering
Purdue University

Ahmed H. Qureshi
Assistant Professor
Department of Computer Science
Purdue University


[1] She, Yu, Chang Li, Jonathon Cleary, and Hai-Jun Su. "Design and fabrication of a soft robotic hand with embedded actuators and sensors." Journal of Mechanisms and Robotics 7, no. 2 (2015).
[2] She, Yu, Ji Chen, Hongliang Shi, and Hai-Jun Su. "Modeling and validation of a novel bending actuator for soft robotics applications." Soft Robotics 3, no. 2 (2016): 71-81.
[3] She, Yu, Sandra Q. Liu, Peiyu Yu, and Edward Adelson. "Exoskeleton-covered soft finger with vision-based proprioception and tactile sensing." In 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 10075-10081. IEEE, 2020.
[4]. A.H. Qureshi, Y. Miao, A. Simeonov, and M.C. Yip. "Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners", IEEE Transactions on Robotics 2020.
[5] A.H. Qureshi, J. Dong, A. Baig, and M.C. Yip. "Constrained Motion Planning Networks X", IEEE Transactions on Robotics 2021