Jinying Lin
Papers
1
Total Citations
7
H-Index
1
About
Jinying Lin is a researcher at the intersection of human-robot interaction and affective computing, with a focus on enabling social robots to learn and express emotional behaviors naturally. Their most cited work, "Affective Behavior Learning for Social Robot Haru with Implicit Evaluative Feedback" (2022, 7 citations), introduces a human-in-the-loop reinforcement learning mechanism that allows robots to learn emotional responses through implicit, natural feedback—such as facial expressions or vocal tones—rather than explicit keyboard or mouse inputs. This contribution is significant because it democratizes robot training, making it accessible to ordinary people without technical expertise. Lin’s research advances the field by bridging the gap between human emotional cues and robotic learning, with potential applications in assistive robotics, education, and therapy. Their work on the social robot Haru exemplifies a shift toward more intuitive, human-centered AI systems. With a growing citation impact, Lin is establishing themselves as a key voice in affective robotics, pushing the boundaries of how machines can understand and mirror human emotions in real-world interactions.
Research Focus
Key Achievements
Top Papers
- 1