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Rianna Jitosho

6 accepted papers

2026

Fourigami: A 4-Degree-Of-Freedom, Force-Controlled, Origami, Finger Pad Haptic Device

Crystal Winston, Hojung Choi, Rianna Jitosho, Zhenishbek Zhakypov, Jasmin Elena Palmer, Mark Cutkosky +1
ICRA 2026poster

Skin deformation haptic devices worn on the finger pad provide realistic touch feedback during interactions with virtual objects. Two primary challenges in creating such devices are: (1) making a multi-degree-of-freedom device that is small and lightweight so it does not encumber the wearer and (2) …

Cited by 0Source ↗Scholar ↗
2023

Hybrid Learning- and Model-Based Planning and Control of In-Hand Manipulation

Rana Soltani Zarrin, Rianna Jitosho, Katsu Yamane
IROS 2023poster

This paper presents a hierarchical framework for planning and control of in-hand manipulation of a rigid object involving grasp changes using fully-actuated multifin-gered robotic hands. While the framework can be applied to the general dexterous manipulation, we focus on a more complex definition o…

Cited by 8Source ↗Scholar ↗
2023

Reinforcement Learning Enables Real-Time Planning and Control of Agile Maneuvers for Soft Robot Arms

Rianna Jitosho, Tyler Ga Wei Lum, Allison Okamura, Karen Liu
CoRL 2023poster

Control policies for soft robot arms typically assume quasi-static motion or require a hand-designed motion plan. To achieve real-time planning and control for tasks requiring highly dynamic maneuvers, we apply deep reinforcement learning to train a policy entirely in simulation, and we identify str…

Cited by 13Source ↗Scholar ↗
2021

ALTRO-C: A Fast Solver for Conic Model-Predictive Control

Brian E. Jackson, Tarun Punnoose, Daniel Neamati, Kevin Tracy, Rianna Jitosho, Zachary Manchester
ICRA 2021poster

Model-predictive control (MPC) is an increasingly popular method for controlling complex robotic systems in which optimal control problems are solved on board the robot at real-time rates. However, successful application of MPC depends critically on the performance of the algorithms used to solve th…

Cited by 27Source ↗Scholar ↗
2019

Exploiting Bistability for High Force Density Reflexive Gripping

Rianna Jitosho, Kevin Choi, Adam Foris, Anirban Mazumdar
ICRA 2019poster

Robotic grasping can enable mobile vehicles to physically interact with the environment for delivery, repositioning, or landing. However, the requirements for grippers on mobile vehicles differ substantially from those used for conventional manipulation. Specifically, grippers for dynamic mobile rob…

Cited by 14Source ↗Scholar ↗