Linda van der Spaa
Papers
4
Total Citations
90
H-Index
3
About
Linda van der Spaa is a leading researcher at the intersection of robotics, human-robot interaction, and ergonomics. Her work focuses on making physical human-robot cooperation both intuitive and safe, with key contributions in ergonomic optimization, preference learning, and elastic actuation. Her most cited work (2020, 55 citations) introduces a novel method for predicting and optimizing ergonomics during bi-manual cooperation tasks, enabling robots to adjust their actions to reduce human physical strain. She also advanced actuator design with an unparameterized optimization approach for parallel elastic actuators (2019, 22 citations), significantly reducing energy loss in robotic systems. More recently, van der Spaa has pioneered inverse reinforcement learning techniques to infer human path and velocity preferences (2023, 11 citations), and developed methods for simultaneously learning intentions and preferences during physical cooperation (2024). Her research directly addresses the challenge of making collaborative robots adaptable to individual human styles and safety margins. With a growing citation impact, van der Spaa’s work is shaping the future of ergonomic, energy-efficient, and human-aware robotics, offering practical solutions for industrial and assistive applications.
Research Focus
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Top Papers
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