Jiaqi Ke
Papers
2
Total Citations
38
H-Index
2
About
Jiaqi Ke is a rising researcher in robotics and computer vision, whose work centers on enabling robots to perceive and interact with 3D articulated objects—such as cabinets, doors, and faucets—in human environments. Their key contributions lie in developing methods for robots to learn manipulation affordances, or the actionable possibilities of objects, through minimal interaction. Ke’s most influential work, “AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-Shot Interactions” (2022), has garnered 36 citations, demonstrating its impact on the field. This research addresses a critical challenge: how can a home-assistant robot, encountering an unfamiliar articulated object, quickly infer how to open, close, or manipulate it? By proposing a framework that adapts affordance learning from just a few interactions, Ke’s work moves beyond static object recognition toward dynamic, functional understanding. This is vital for robots that must operate in unstructured homes, where every cabinet or faucet may differ. Ke’s research bridges perception and action, offering a practical path toward more capable and adaptable service robots. Their focus on few-shot learning and affordance adaptation marks a significant step in making robotic assistance in daily tasks a tangible reality.
Research Focus
Key Achievements
Top Papers
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