High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET
This addresses the problem of enabling robots to follow natural language directions more effectively, though it appears incremental as it builds on existing pointer network and attention methods.
The paper tackles the problem of translating natural language instructions into high-level plans for robot navigation by modeling the environment as a graph and using a modified R-NET with attention mechanisms. The result shows that their model outperforms the state-of-the-art approach in tests on a navigation graph dataset for both known and unknown environments.
When the navigational environment is known, it can be represented as a graph where landmarks are nodes, the robot behaviors that move from node to node are edges, and the route is a set of behavioral instructions. The route path from source to destination can be viewed as a class of combinatorial optimization problems where the path is a sequential subset from a set of discrete items. The pointer network is an attention-based recurrent network that is suitable for such a task. In this paper, we utilize a modified R-NET with gated attention and self-matching attention translating natural language instructions to a high-level plan for behavioral robot navigation by developing an understanding of the behavioral navigational graph to enable the pointer network to produce a sequence of behaviors representing the path. Tests on the navigation graph dataset show that our model outperforms the state-of-the-art approach for both known and unknown environments.