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  • Academic
    University - Student Department: Mechanical Engineering Position: Graduate

Additional Info

  • Mail Code: 3030

All Publications

  • phloSAR: a Portable, High-Flow Pressure Supply and Regulator Enabling Untethered Operation of Large Pneumatic Soft Robots Ahlquist, M., Jitosho, R., Bao, J., Okamura, A. M., IEEE IEEE. 2024: 28-33

    View details for DOI 10.1109/ROBOSOFT60065.2024.10522055

    View details for Web of Science ID 001230033600142

  • Passive Shape Locking for Multi-Bend Growing Inflated Beam Robots Jitosho, R., Simon-Trench, S., Okamura, A. M., Do, B. H., IEEE IEEE. 2023

    View details for DOI 10.1109/ROBOSOFT55895.2023.10122027

    View details for Web of Science ID 001008269300076

  • Reinforcement Learning Enables Real-Time Planning and Control of Agile Maneuvers for Soft Robot Arms Jitosho, R., Lum, T., Okamura, A. M., Liu, C., Tan, J., Toussaint, M., Darvish, K. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023

    View details for Web of Science ID 001221201501010

  • Hybrid Learning- and Model-Based Planning and Control of In-Hand Manipulation Zarrin, R., Jitosho, R., Yamane, K., IEEE IEEE. 2023: 8720-8726

    View details for DOI 10.1109/IROS55552.2023.10342153

    View details for Web of Science ID 001136907802122

  • Mechanical Imaging of Soft Tissues With Miniature Climbing Robots IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Dementyev, A., Jitosho, R., Paradiso, J. A. 2021; 68 (10): 3142-3150

    Abstract

    Systematically mapping the mechanical properties of skin and tissue is useful for biomechanics research and disease diagnostics. For example, later stage breast cancer and lymphoma manifest themselves as hard nodes under the skin. Currently, mechanical measurements are done manually, with a sense of touch or a handheld tool. Manual measurements do not provide quantitative information and vary depending on the skill of the practitioner. Research shows that tactile sensors could be more sensitive than a hand. We propose a method that uses our previously developed skin-crawling robots to noninvasively test the mechanical properties of soft tissue. Robots are more systematic and repeatable than humans. Using the data collected with a cutomoter or indenter integrated into the miniature robot, we trained a convolutional neural network to classify the size and depth of the lumps. The classification works with 98.8% accuracy for cutometer and 99.6% for indenter for lump size with a diameter of 0 to 10 mm embedded in depth of 1 to 5 mm in a simulated tissue. We conducted a limited evaluation on a forearm, where the robot imaged dry skin with a cutometer. We hope to improve the ability to test tissues noninvasively, and ultimately provide better sensitivity and systematic data collection.

    View details for DOI 10.1109/TBME.2021.3070585

    View details for Web of Science ID 000697820800029

    View details for PubMedID 33798064

  • A Dynamics Simulator or Soft Growing Robots Jitosho, R., Agharese, N., Okamura, A., Manchester, Z., IEEE IEEE. 2021: 11775-11781

    View details for DOI 10.1109/ICRA48506.2021.9561420

    View details for Web of Science ID 000771405404012

  • ALTRO-C: A Fast Solver for Conic Model-Predictive Control Jackson, B. E., Punnoose, T., Neamati, D., Tracy, K., Jitosho, R., Manchester, Z., IEEE IEEE. 2021: 7357-7364

    View details for DOI 10.1109/ICRA48506.2021.9561438

    View details for Web of Science ID 000771405401010

  • Design and Testing of an Ultra-Light Weight Perching System for Sloped or Vertical Rough Surfaces on Mars Backus, S., Izraelevitz, J., Quan, J., Jitosho, R., Slavick, E., Kalantari, A., IEEE IEEE. 2020

    View details for Web of Science ID 000681699101059