PanoDP: Learning Collision-Free Navigation with Panoramic Depth and Differentiable Physics

TL;DR

PanoDP is a novel learning framework that uses panoramic depth perception and differentiable physics to improve collision-free navigation in cluttered environments, demonstrating superior performance and generalization.

Contribution

It introduces a communication-free, panoramic depth-based learning method with differentiable physics for safe navigation, enhancing training stability and out-of-distribution robustness.

Findings

Increases collision-free and completion rates over baselines.

Leverages 360-degree information effectively.

Shows strong generalization in external simulators.

Abstract

Autonomous collision-free navigation in cluttered environments requires safe decision-making under partial observability with both static structure and dynamic obstacles. We present \textbf{PanoDP}, a communication-free learning framework that combines four-view panoramic depth perception with differentiable-physics-based training signals. PanoDP encodes panoramic depth using a lightweight CNN and optimizes policies with dense differentiable collision and motion-feasibility terms, improving training stability beyond sparse terminal collisions. We evaluate PanoDP on a controlled ring-to-center benchmark with systematic sweeps over agent count, obstacle density/layout, and dynamic behaviors, and further test out-of-distribution generalization in an external simulator (e.g., AirSim). Across settings, PanoDP increases collision-free and completion rates over single-view and non-physics-guided baselines under matched training budgets, and ablations (view masking, rotation augmentation) confirm the policy leverages 360-degree information. Code will be open source upon acceptance.