Path-following navigation network using sparse visual memory

Following a demonstration path without observing exact location of an agent is a challenging navigation problem. Especially, considering the probabilistic transition function of the agent makes the problem hard to solve with an exact action decision, so learning-based approaches have been used to solve this task. For example, a previous method by Kumar and Gupta et al., robust path following network (RPF), is a neural-network-based method using visual memories of the demonstration. Although the RPF shows good performances on the path-following task, it does not consider the efficiency of the visual memory since it requires the entire visual memory of the demonstration. In this paper, we propose a path-following network using sparse memory of the demonstration path that can deal with various sparsity of the visual memory. For each time step, the proposed network makes soft attention on the sparse memory to control the agent. We test the proposed model on the Habitat simulator using MatterPort3D dataset with various sparsity of memory. The experimental results show that the proposed method achieves 81.9% of success rate and 73.7% of SPL on a model with 0.8 memory sparsity, and also the results of the models with other memory sparsity achieve reasonable performances compare to the baseline methods.