Jiren Jin
Papers
1
Total Citations
5
H-Index
1
About
Jiren Jin is a researcher advancing the frontiers of robotic perception and continual learning, with a primary focus on enabling robots to operate intelligently in dynamic, unstructured home environments. His key research areas include object recognition, domain adaptation, and open-set learning—critical for robots that must adapt to new settings without forgetting prior knowledge. His most cited work, "Object Recognition with Continual Open Set Domain Adaptation for Home Robot" (2021, 5 citations), tackles the challenge of how robots can recognize familiar objects while ignoring novel, irrelevant ones—a fundamental skill for tasks like object searching. This contribution addresses the "open set" problem, where robots encounter unknown objects, and the "continual" challenge of learning without catastrophic forgetting. By bridging domain adaptation and open-set recognition, Jin’s work lays groundwork for more robust, human-like robotic perception in real-world homes. His research is particularly notable for its practical orientation, aiming to make home robots more autonomous and reliable. With growing interest in embodied AI and lifelong learning, Jin’s contributions are poised to influence both robotics and computer vision communities.
Research Focus
Key Achievements
Top Papers
- 1