Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation
This work addresses the sim-to-real transfer challenge in robotic navigation, which is crucial for deploying learning-based agents in real-world indoor settings, though it is incremental as it builds on existing methods with new combinations and validation tools.
The paper tackles the problem of transferring navigation policies from simulation to real-world environments by proposing a benchmark of deep learning architectures that fuse multiple mid-level visual representations for PointGoal navigation using reinforcement learning, showing that multi-modal input improves performance and that a validation tool can efficiently estimate real-world navigation outcomes while saving resources.
Navigating complex indoor environments requires a deep understanding of the space the robotic agent is acting into to correctly inform the navigation process of the agent towards the goal location. In recent learning-based navigation approaches, the scene understanding and navigation abilities of the agent are achieved simultaneously by collecting the required experience in simulation. Unfortunately, even if simulators represent an efficient tool to train navigation policies, the resulting models often fail when transferred into the real world. One possible solution is to provide the navigation model with mid-level visual representations containing important domain-invariant properties of the scene. But, what are the best representations that facilitate the transfer of a model to the real-world? How can they be combined? In this work we address these issues by proposing a benchmark of Deep Learning architectures to combine a range of mid-level visual representations, to perform a PointGoal navigation task following a Reinforcement Learning setup. All the proposed navigation models have been trained with the Habitat simulator on a synthetic office environment and have been tested on the same real-world environment using a real robotic platform. To efficiently assess their performance in a real context, a validation tool has been proposed to generate realistic navigation episodes inside the simulator. Our experiments showed that navigation models can benefit from the multi-modal input and that our validation tool can provide good estimation of the expected navigation performance in the real world, while saving time and resources. The acquired synthetic and real 3D models of the environment, together with the code of our validation tool built on top of Habitat, are publicly available at the following link: https://iplab.dmi.unict.it/EmbodiedVN/