Embodied Perception for Test-time Grasping Detection Adaptation with Knowledge Infusion

It has always been expected that a robot can be easily deployed to unknown scenarios, accomplishing robotic grasping tasks without human intervention. Nevertheless, existing grasp detection approaches are typically off-body techniques and are realized by training various deep neural networks with extensive annotated data support. {In this paper, we propose an embodied test-time adaptation framework for grasp detection that exploits the robot's exploratory capabilities.} The framework aims to improve the generalization performance of grasping skills for robots in an unforeseen environment. Specifically, we introduce embodied assessment criteria based on the robot's manipulation capability to evaluate the quality of the grasp detection and maintain suitable samples. This process empowers the robots to actively explore the environment and continuously learn grasping skills, eliminating human intervention. Besides, to improve the efficiency of robot exploration, we construct a flexible knowledge base to provide context of initial optimal viewpoints. Conditioned on the maintained samples, the grasp detection networks can be adapted in the test-time scene. When the robot confronts new objects, it will undergo the same adaptation procedure mentioned above to realize continuous learning. Extensive experiments conducted on a real-world robot demonstrate the effectiveness and generalization of our proposed framework.