VDS-Nav: Volumetric Depth-Based Safe Navigation for Aerial Robots–Bridging the Sim-To-Real Gap

Abstract

End-to-end navigation via deep reinforcement learning has become a key approach for vision-based tasks. However, the sim-to-real gap remains a challenge, especially for aerial robots, where policies trained in simulation often fail in real-world environments. In this work, we propose a novel navigation paradigm -- volumetric depth-based safe navigation(VDS-Nav), which trains a policy to infer linear velocities and yaw rate directly from a sequence of depth images, bypassing the need for a pre-trained latent space encoder. We enhance safety with a depth-based reward design, enabling the seamless incorporation of system constraints via logarithmic barrier function methods. Most importantly, using explicit sensor information in our reward design leads to seamless sim-to-real transfer by strengthening the correlation between state-action pairs and received rewards. To evaluate the effectiveness of VDS-Nav, we compare it to a baseline that first trains a variational autoencoder to encode depth images into a latent space for policy training. The simulation results show that VDS-Nav outperforms the baseline in terms of success rate. Furthermore, real-world experiments validate the policy, with real-time performance closely matching simulation results, suggesting an effective sim-to-real transfer