Visual Memory for Robust Path Following
This addresses the challenge of reliable navigation in uncertain environments for robotics, though it appears incremental as it builds on existing learning-based techniques.
The paper tackles the problem of robust path following and homing under noisy actuation and environmental changes by introducing an end-to-end learning approach with two networks, achieving superior performance over classical and other learning-based methods in realistic simulators.
Humans routinely retrace paths in a novel environment both forwards and backwards despite uncertainty in their motion. This paper presents an approach for doing so. Given a demonstration of a path, a first network generates a path abstraction. Equipped with this abstraction, a second network observes the world and decides how to act to retrace the path under noisy actuation and a changing environment. The two networks are optimized end-to-end at training time. We evaluate the method in two realistic simulators, performing path following and homing under actuation noise and environmental changes. Our experiments show that our approach outperforms classical approaches and other learning based baselines.