Giulia Campinoti

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About

Giulia Campinoti is a rising star in soft robotics, whose work is redefining how compliant machines sense, adapt, and interact with the physical world. Her research centers on three key areas: control strategies for dynamic tasks, online learning for adaptive manipulation, and textile-based proprioception for soft systems. Campinoti’s major contributions include the development of **SoftSling**, a bio-inspired control strategy that enables a soft robotic arm to throw objects with precision using circular run-ups—a feat that mimics ancient slingers and demonstrates unprecedented dynamic capability in soft hardware. She also introduced an **error-driven adaptive scheme** employing an Online Regressing Network (ORN), allowing soft arms to adjust their control policies in real time without retraining. Her most cited work, “Policy Adaptation using an Online Regressing Network in a Soft Robotic Arm” (2023, 13 citations), and the follow-up “SoftSling” (2024, 10 citations) have already garnered attention for bridging machine learning and soft actuation. Most recently, in **SoftTex** (2025), Campinoti pioneered a learning-based textile proprioception system that embeds sensing directly into the robot’s fabric, preserving compliance while enabling accurate state estimation—a critical step toward safe biomedical applications. Her work is shaping a future where soft robots are not only flexible but also intelligent and dexterous.