Papers
5
Total Citations
98
H-Index
4
About
Jenny Benois-Pineau’s research lies at the intersection of human-robot interaction, computer vision, and assistive technology, with a focus on enabling intuitive control of robotic and prosthetic arms. Her major contributions include the development of the Reachy platform—a 3D-printed human-like robotic arm that serves as a testbed for evaluating control strategies, earning 75 citations. She has also pioneered gaze-driven action forecasting, using asymmetric multi-task learning to predict grasping intentions from human attention, with direct applications for people with motor and cognitive disabilities. To support these advances, she created the 3D-ARM-Gaze dataset, a public resource capturing natural arm reaching movements paired with gaze data in virtual reality, which has already garnered 10 citations. Her work extends to wearable computing, where she has implemented FPGA-based SIFT algorithms for real-time visual analysis in low-power, portable prosthetics. By combining robotics, machine learning, and human perception, Benois-Pineau’s research is shaping the future of assistive robotics, making her a key figure in the development of intelligent, human-aware robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3FPGA-based SIFT implementation for wearable computing5 citations · 2019
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