Xiaobo Hu

Xiaobo Hu, Youfang Lin, HeHe Fan et al.

Given an object of interest, visual navigation aims to reach the object's location based on a sequence of partial observations. To this end, an agent needs to 1) learn a piece of certain knowledge about the relations of object categories in the world during training and 2) look for the target object based on the pre-learned object category relations and its moving trajectory in the current unseen environment. In this paper, we propose a Category Relation Graph (CRG) to learn the knowledge of object category layout relations and a Temporal-Spatial-Region (TSR) attention architecture to perceive the long-term spatial-temporal dependencies of objects helping the navigation. We learn prior knowledge of object layout, establishing a category relationship graph to deduce the positions of specific objects. Subsequently, we introduced TSR to capture the relationships of objects in temporal, spatial, and regions within the observation trajectories. Specifically, we propose a Temporal attention module (T) to model the temporal structure of the observation sequence, which implicitly encodes the historical moving or trajectory information. Then, a Spatial attention module (S) is used to uncover the spatial context of the current observation objects based on the category relation graph and past observations. Last, a Region attention module (R) shifts the attention to the target-relevant region. Based on the visual representation extracted by our method, the agent can better perceive the environment and easily learn superior navigation policy. Experiments on AI2-THOR demonstrate our CRG-TSR method significantly outperforms existing methods regarding both effectiveness and efficiency. The code has been included in the supplementary material and will be publicly available.

Xiaobo Hu, Youfang Lin, Yue Liu et al.

Visual reinforcement learning has proven effective in solving control tasks with high-dimensional observations. However, extracting reliable and generalizable representations from vision-based observations remains a central challenge. Inspired by the human thought process, when the representation extracted from the observation can predict the future and trace history, the representation is reliable and accurate in comprehending the environment. Based on this concept, we introduce a Bidirectional Transition (BiT) model, which leverages the ability to bidirectionally predict environmental transitions both forward and backward to extract reliable representations. Our model demonstrates competitive generalization performance and sample efficiency on two settings of the DeepMind Control suite. Additionally, we utilize robotic manipulation and CARLA simulators to demonstrate the wide applicability of our method.

Xiaobo Hu, Youfang Lin, Shuo Wang et al.

Object visual navigation aims to steer an agent toward a target object based on visual observations. It is highly desirable to reasonably perceive the environment and accurately control the agent. In the navigation task, we introduce an Agent-Centric Relation Graph (ACRG) for learning the visual representation based on the relationships in the environment. ACRG is a highly effective structure that consists of two relationships, i.e., the horizontal relationship among objects and the distance relationship between the agent and objects . On the one hand, we design the Object Horizontal Relationship Graph (OHRG) that stores the relative horizontal location among objects. On the other hand, we propose the Agent-Target Distance Relationship Graph (ATDRG) that enables the agent to perceive the distance between the target and objects. For ATDRG, we utilize image depth to obtain the target distance and imply the vertical location to capture the distance relationship among objects in the vertical direction. With the above graphs, the agent can perceive the environment and output navigation actions. Experimental results in the artificial environment AI2-THOR demonstrate that ACRG significantly outperforms other state-of-the-art methods in unseen testing environments.

Leo Yu Zhang, Xiaobo Hu, Yuansheng Liu et al.

This paper presents a novel efficient chaotic image encryption scheme, in which the temp-value feedback mechanism is introduced to the permutation and diffusion procedures. Firstly, a simple trick is played to map the plain-image pixels to the initial condition of the Logistic map. Then, a pseudorandom number sequence (PRNS) is obtained from iterating the map. The permutation procedure is carried out by a permutation sequence which is generated by comparing the PRNS and its sorted version. The diffusion procedure is composed of two reversely executed rounds. During each round, the current plain-image pixel and the last cipher-image pixel are used to produce the current cipher-image pixel with the help of the Logistic map and a pseudorandom number generated by the Chen system. To enhance the efficiency, only expanded XOR operation and modulo 256 addition are employed during diffusion. Experimental results show that the new scheme owns a large key space and can resist the differential attack. It is also efficient.