Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning
This addresses the problem of real-time control for autonomous navigation in robotics, but it is incremental as it builds on existing imitation learning and mapping techniques.
The paper tackled the problem of following high-level navigation instructions on a simulated quadcopter by mapping images, instructions, and pose estimates directly to continuous low-level velocity commands, showing that their method outperforms strong neural baselines and almost reaches expert policy performance.
We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control. The Grounded Semantic Mapping Network (GSMN) is a fully-differentiable neural network architecture that builds an explicit semantic map in the world reference frame by incorporating a pinhole camera projection model within the network. The information stored in the map is learned from experience, while the local-to-world transformation is computed explicitly. We train the model using DAggerFM, a modified variant of DAgger that trades tabular convergence guarantees for improved training speed and memory use. We test GSMN in virtual environments on a realistic quadcopter simulator and show that incorporating an explicit mapping and grounding modules allows GSMN to outperform strong neural baselines and almost reach an expert policy performance. Finally, we analyze the learned map representations and show that using an explicit map leads to an interpretable instruction-following model.