Locally-Connected Interrelated Network: A Forward Propagation Primitive

Abstract

End-to-end learning for planning is a promising approach for finding good robot strategies in situations where the state transition, observation, and reward functions are initially unknown. Many neural network architectures for this approach have shown positive results. Across these networks, seemingly small components have been used repeatedly in different architectures, which means improving the efficiency of these components has great potential to improve the overall performance of the network. This paper aims to improve one such component: The forward propagation module. In particular, we propose Locally-Connected Interrelated Network (LCI-Net)—a novel type of locally connected layer with unshared but interrelated weights—to improve the efficiency of information propagation and learning stochastic transition models for planning. LCI-Net is a small differentiable neural network module that can be plugged into various existing architectures. For evaluation purposes, we apply LCI-Net to QMDP-Net; QMDP-Net is a neural network for solving POMDP problems whose transition, observation, and reward functions are learned. Simulation tests on benchmark problems involving 2D and 3D navigation and grasping indicate promising results: Changing only the forward propagation module alone with LCI-Net improves QMDP-Net generalization capability by a factor of up to 10.