Yanjie Liu

Liulong Ma, Yanjie Liu, Jiao Chen et al.

Autonomous navigation is an essential capability of smart mobility for mobile robots. Traditional methods must have the environment map to plan a collision-free path in workspace. Deep reinforcement learning (DRL) is a promising technique to realize the autonomous navigation task without a map, with which deep neural network can fit the mapping from observation to reasonable action through explorations. It should not only memorize the trained target, but more importantly, the planner can reason out the unseen goal. We proposed a new motion planner based on deep reinforcement learning that can arrive at new targets that have not been trained before in the indoor environment with RGB image and odometry only. The model has a structure of stacked Long Short-Term memory (LSTM). Finally, experiments were implemented in both simulated and real environments. The source code is available: https://github.com/marooncn/navbot.

Liulong Ma, Yanjie Liu*, Jiao Chen

Robot navigation in mapless environment is one of the essential problems and challenges in mobile robots. Deep reinforcement learning is a promising technique to tackle the task of mapless navigation. Since reinforcement learning requires a lot of explorations, it is usually necessary to train the agent in the simulator and then migrate to the real environment. The big reality gap makes RGB image, the most common visual sensor, rarely used. In this paper we present a learning-based mapless motion planner by taking RGB images as visual inputs. Many parameters in end-to-end navigation network taking RGB images as visual input are used to extract visual features. Therefore, we decouple visual features extracted module from the reinforcement learning network to reduce the need of interactions between agent and environment. We use Variational Autoencoder (VAE) to encode the image, and input the obtained latent vector as low-dimensional visual features into the network together with the target and motion information, so that the sampling efficiency of the agent is greatly improved. We built simulation environment as robot navigation environment for algorithm comparison. In the test environment, the proposed method was compared with the end-to-end network, which proved its effectiveness and efficiency. The source code is available: https://github.com/marooncn/navbot.

Ziyuan Zhao, Zeyu Ma, Yanjie Liu et al.

Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring. Recently, deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical image segmentation. However, 2D DCNNs cannot fully leverage the inter-slice information, while 3D DCNNs are computationally expensive and memory intensive. To address these issues, we first propose a novel dense-sparse training flow from a data perspective, in which, densely adjacent slices and sparsely adjacent slices are extracted as inputs for regularizing DCNNs, thereby improving the model performance. Moreover, we design a 2.5D light-weight nnU-Net from a network perspective, in which, depthwise separable convolutions are adopted to improve the efficiency. Extensive experiments on the LiTS dataset have demonstrated the superiority of the proposed method.