Max Robotics — Global Service Robot Directory + US Certification

About

Meeko Oishi is a leading researcher in stochastic reachability analysis and its application to safe autonomous systems, particularly in dynamic and uncertain environments. Her work bridges rigorous control theory with practical robotics, addressing the fundamental challenge of ensuring safety despite unpredictable obstacles and human interaction. Oishi’s most impactful contribution is the development of a hybrid dynamic moving obstacle avoidance framework, which uses a stochastic reachable set-based potential field to guarantee collision-free paths even when obstacles change behavior without warning—a method that has garnered 169 citations. She has pioneered efficient computational techniques using Fourier transforms to compute forward stochastic reach probability measures, enabling real-time motion planning for nonlinear systems with affine disturbances. Her work on probabilistic occupancy functions and successive convexification has advanced stochastic motion planning, achieving real-time performance in cluttered environments. Beyond robotics, Oishi applies her expertise to human-automation systems, validating cognitive models for collaborative hybrid systems and developing model predictive control strategies to mitigate Parkinson’s tremor in powered wheelchairs. Her research, with over 270 total citations, is essential reading for anyone working on safety-critical autonomy, human-robot interaction, or stochastic control.