Papers
3
Total Citations
44
H-Index
3
About
Dr. Jishen Bai is a rising researcher in robotics and human-robot interaction, with a focus on enabling robots to learn and adapt in collaborative settings. His work bridges machine learning and manipulation, particularly through domain adaptation and learning from demonstration. In his highly cited 2021 paper, Bai introduced a domain adversarial transfer method for grasp pose detection, allowing robots to generalize across different visual domains and task constraints—a key step toward robust, real-world manipulation. His 2022 study on environment-adaptive learning from demonstration further advanced proactive assistance in human-robot collaborative tasks, demonstrating how robots can adjust their behavior based on changing contexts, earning 20 citations. Most recently, Bai proposed hierarchical kernelized movement primitives for learning collaborative trajectories in object handover, addressing the nuanced coordination required in shared tasks. With over 44 citations across his top works, Bai’s contributions are shaping the next generation of adaptive, socially aware robots.
Research Focus
Key Achievements
Top Papers
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