Vision-based proprioception and tactile sensing for soft robots
Zhang, Ruohan
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https://hdl.handle.net/2142/132632
Description
Title
Vision-based proprioception and tactile sensing for soft robots
Author(s)
Zhang, Ruohan
Issue Date
2025-12-12
Director of Research (if dissertation) or Advisor (if thesis)
Yuan, Wenzhen
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Tactile Sensing
Robot Perception
Abstract
Soft pneumatic manipulators are attractive for industrial and human-interactive tasks because of their inherent compliance, yet practical deployment demands accurate proprioception and tactile feedback. This thesis introduces a compact, vision-based sensing framework that delivers both modalities from a single internal camera. We instantiate the approach on PneuGelSight, a pneumatically actuated finger that uses color-coded illumination and a reflective elastomer surface to encode deformation and contact geometry in one image.
To co-design hardware and perception, we develop a simulation pipeline that couples finite-element deformation with physics-based optical rendering, enabling design optimization and training data generation. The resulting models provide high-resolution proprioceptive shape estimation and dense tactile reconstruction, transferring from simulation to hardware without per-scene supervision (zero-shot). Experiments demonstrate accurate recovery of large-scale bends, robust contact mapping under varied loads, and practical multi-touch object reconstruction, while keeping hardware simple and lightweight.
Together, PneuGelSight and the sim-to-real pipeline offer an easily implementable and robust sensing methodology for soft robots, advancing the integration of rich feedback into compliant manipulators and opening paths to closed-loop control and scalable multi-finger systems.
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