Shail Jadav
Papers
5
Total Citations
25
H-Index
3
About
Shail Jadav is a rising researcher in the field of robotic manipulation and human-robot collaboration, with a focus on making physical interactions safer, more adaptive, and more efficient. His core research centers on **variable impedance control**, **shared autonomy**, and **contact-rich manipulation**, particularly for complex tasks like assembly and disassembly. Jadav’s major contributions include developing a **configuration and force-field aware variable impedance control** framework that enables robots to learn from human demonstrations and quickly re-learn skills after perturbations, reducing initial errors by up to 50% in divergent force fields (10 citations). He also introduced the **REASSEMBLE multimodal dataset** (7 citations), a benchmark for contact-rich robotic assembly and disassembly, and proposed a **shared autonomy framework** using virtual potential fields for encoding human demonstrations in co-manufacturing tasks like furniture assembly (5 citations). Additionally, his work on **utilizing manipulator redundancy for torque reduction** during force interaction (1 citation) addresses a critical safety and efficiency challenge in human-shared environments. With over 25 total citations and multiple first-author publications in top venues like ICRA and IROS, Jadav is establishing himself as a key contributor to the next generation of compliant, learning-enabled robots.
Research Focus
Key Achievements
Top Papers
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