Planning under Noise with Neural Operators

Imagine you're designing a robot that needs to navigate through a cluttered space, but the environment is unpredictable — obstacles might shift, and the robot's movements can be noisy. Traditional planning methods often struggle in these scenarios because they rely on fixed paths and can easily get stuck or misled by noise, which is what's called planning failure. When the robot encounters an obstacle, it might not know how to adjust its route effectively, leading to inefficient navigation or even complete failure to reach its goal. This is particularly problematic in complex environments where the layout can change unexpectedly.

To address these challenges, the authors propose a method called Planning Under Noise (PUN), which learns to create noise-aware value fields that help the robot plan its path more effectively. Instead of getting stuck at obstacles, PUN uses a clever approach to allow paths to explore freely until they reach the target, even in the presence of noise. By leveraging a Monte Carlo solver that learns from noisy data, PUN can efficiently compute paths without needing extensive recalibration for every new scenario. In tests, PUN outperformed existing planners in terms of success rates and speed, particularly in high-noise situations, making it a promising tool for anyone building navigation systems in unpredictable environments.

The approach introduces a novel way to handle planning under noise using learned value fields.

The evaluation shows strong performance across multiple noise levels and dimensions, though it lacks extensive baseline comparisons.