Task Oriented Grasping

Task-oriented grasping (TOG) focuses on enabling robots to grasp objects in ways that optimally facilitate subsequent manipulation tasks, going beyond simple object detection. Current research emphasizes learning generalizable TOG strategies from diverse data sources, including human demonstrations and large language models, often employing neural networks (e.g., GANs, implicit neural fields) and foundation models to represent object shapes, task semantics, and grasp affordances. This field is crucial for advancing robotic manipulation capabilities in unstructured environments, with implications for assistive robotics, industrial automation, and other applications requiring flexible and adaptable robotic systems.