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MSR Thesis Proposal


Yichen Li MSR Student Robotics Institute,
Carnegie Mellon University
Wednesday, March 15
11:00 am to 12:00 pm
NSH 3305
MSR Thesis Talk: Yichen Li

Title: Simulation-guided Design for Vision-based Tactile Sensing on a Soft Robot Finger


Soft pneumatic robot manipulators have garnered widespread interest due to their compliance and flexibility, which enable soft, non-destructive grasping and strong adaptability to complex working environments. Tactile sensing is crucial for these manipulators to provide real-time contact information for control and manipulation. Vision-based tactile sensing has been used in soft pneumatic robot manipulators to achieve high spatial resolution of contact information. However, designing a high-quality vision-based tactile sensor for soft pneumatic robot manipulators is challenging because of the manipulator’s deformation during pneumatic actuation.

In this thesis, we present a physics-based optical simulation pipeline to guide the design of vision-based tactile sensing on a soft robot finger. Our simulation pipeline enables faster iteration cycles and automatic optimization of design parameters, eliminating the need to manufacture the entire robot finger for each design iteration. The simulation utilizes physics-based rendering (PBR) with highly accurate optical modeling of the robot finger to ensure that performance improvements in tactile sensing in the simulation are transferrable to the real-world robot finger. We introduce a fast numerical metric to test tactile sensing performance at different robot actuation statuses and contact locations to evaluate designs. We rely on human expertise to perform a coarse search on initial soft manipulator designs and then select the best based on defined metrics. Then, we apply the covariance matrix adaptation evolution strategy (CMA-ES) as a numerical optimization method to iteratively fine-tune design parameters. We compare the tactile sensing performance of the optimized design and the initial design in both simulation and real-world setup and demonstrate an improvement in both cases.



Prof. Wenzhen Yuan (advisor)

Prof. Matthew O’Toole

Arpit Agarwal