Most existing 6-DoF robot grasping solutions depend on strong supervision on grasp pose to ensure satisfactory performance, which could be laborious and impractical when the robot works in some restricted area. To this end, we propose a self-supervised 6-DoF grasp pose detection framework via an Augmented Reality (AR) teleoperation system that can efficiently learn human demonstrations and provide 6-DoF grasp poses without grasp pose annotations. Specifically, the system collects the human demonstration from the AR environment and contrastively learns the grasping strategy from the demonstration. For the real-world experiment, the proposed system leads to satisfactory grasping abilities and learning to grasp unknown objects within three demonstrations.

Different from the open set recognition, generalized open set recognition learns the most similar known classes for unseen samples using known classes samples and side in-formation of known classes. It is challenging because hierarchically structured side information is distorted when features are embedded in the Euclidean space in existing literature, which incurs the difficulty of identifying the unseen samples. In this paper, we introduce a side information learning algorithm for generalized open set recognition based on the hyperbolic space to alleviate the distortion and accurately identify the unknown samples. Specifically, we propose a hyperbolic side information learning framework to identify the unseen samples and an ancestor search algorithm to search the most similar ancestor from the taxonomy of selected known classes. Experiments on CUB-200 and AWA 2 datasets show that our method improves the performance of generalized open set recognition by a large margin.

Most existing video anomaly detection methods are dependent on strong supervision to achieve satisfactory performance, which could be laborious and impractical. Besides, methods using weakly supervised learning less consider the relations among event proposals. To this end, we propose an anomaly event detection method by the instance-based event proposal generation and the proposal relation learning. Specifically, the event proposals are generated by sampling temporal distributions from the multiple instance learning (MIL), while relations among the proposals are captured by graph convolutional network for anomaly localization and classification. The proposed method is free from strong frame-level supervision but only requires video-level annotations. We conduct experiments on four datasets, i.e., UCF crime, UCSD-Peds, UMN, and CityScene, and show state-of-the-art performance on anomaly recognition and detection tasks.