Inbar Ben-David
Papers
2
Total Citations
27
H-Index
2
About
Inbar Ben-David is a roboticist whose work sits at the intersection of haptics, manipulation, and human-robot collaboration. Her research focuses on enabling robots to perceive and interact with the physical world without relying solely on vision—a critical capability for real-world tasks where visual occlusion is common. In her highly cited 2022 paper (15 citations), she tackled the challenge of object pose estimation for underactuated, compliant robotic hands, developing a haptic-based control method that allows these inherently uncertain systems to manipulate objects in-hand with greater precision. This work directly addresses a key limitation in soft robotics, where traditional modeling is difficult. Complementing this, her 2021 study (12 citations) introduced a wearable force-myography device for robust, multi-user in-hand object recognition during human-robot collaboration. By decoding human intention through muscle activity rather than sight, her approach enables more intuitive and seamless shared work. Together, these contributions form a compelling body of work that pushes toward more dexterous, perceptive, and collaborative robotic systems—advancing the field beyond vision-centric paradigms toward richer, multimodal interaction.
Research Focus
Key Achievements
Top Papers
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