Filippo Spinelli

Papers

Total Citations

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About

Filippo Spinelli is a roboticist whose research pushes the boundaries of control and autonomy for complex, real-world robotic systems. His work is distinguished by a focus on two challenging domains: soft continuum manipulators and heavy-duty hydraulic machinery. In his highly cited 2022 work, Spinelli introduced a unified Model Predictive Control (MPC) framework for soft robots, a breakthrough that elegantly handles nonlinear actuation dynamics, motion constraints, and variable stiffness—all within a single algorithm. This foundational contribution, with 14 citations, provides a critical tool for enabling the safe, compliant, and precise control that soft robots promise for human interaction. Spinelli’s impact extends to industrial automation, where he tackles the problem of dynamic manipulation with heavy material handling machines. His 2024 paper demonstrates how Reinforcement Learning can teach these massive, hydraulically-actuated systems to perform dynamic throwing motions, leveraging passive joints to dramatically improve speed and workspace. This work, already garnering 6 citations, marks a significant step beyond slow, semi-static cycles toward agile, efficient automation. By bridging advanced control theory and learning-based methods, Spinelli is shaping a future where robots—from flexible surgical tools to powerful construction equipment—operate with unprecedented dexterity and intelligence.