2026-09-20T16:46:17Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/132632 2026-03-24 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01 The student, Ruohan Zhang, accepted the attached license on 2025-12-11 at 16:26. The student, Ruohan Zhang, submitted this Thesis for approval on 2025-12-11 at 16:27. This Thesis was approved for publication on 2025-12-12 at 08:22. DSpace SAF Submission Ingestion Package generated from Vireo submission #22831 on 2026-02-19 at 18:45:37 Vision-based proprioception and tactile sensing for soft robots Zhang, Ruohan 2025-12-12 Yuan, Wenzhen Tactile Sensing Robot Perception eng 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. 2025-12 Thesis https://hdl.handle.net/2142/132632 Copyright 2025 Ruohan Zhang Electrical & Computer Eng Electrical & Computer Engr University of Illinois Urbana-Champaign M.S. Thesis