Stephan Stansfield

IIT@MIT, Massachusetts Institute of Technology

Papers

Total Citations

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About

Stephan Stansfield investigates the computational principles underlying human motor control, with a particular focus on how people skillfully manipulate complex, nonlinear objects—such as liquids in containers or flexible clothing—that challenge traditional control theories. His work reveals that humans rely on simplified internal models to manage these underactuated dynamics, a finding that bridges robotics, neuroscience, and psychology. In a 2024 study (5 citations), he demonstrated that predictive model-based strategies are employed even in unconstrained tasks, offering new insights into the brain’s ability to approximate and adapt to physical complexity. Stansfield also addresses critical challenges in assistive technology, notably in a 2023 paper (2 citations) exploring the “user perception gap” among older adults using sit-to-stand assistance devices. By identifying mismatches between engineering design and user expectations, his research highlights barriers to adoption in medical robotics. Though early in his career, Stansfield’s contributions are shaping how we understand human dexterity and human-robot interaction, with implications for rehabilitation, prosthetics, and autonomous systems. His work stands out for its interdisciplinary rigor and practical relevance to aging populations.