To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation

Understand

It is important for robots to be able to decide whether they can go through a space or not, as they navigate through a dynamic environment.

• This capability can help them avoid injury or serious damage, e.g., as a result of running into people and obstacles, getting stuck, or falling off an edge.
• To this end, we propose an unsupervised and a near-unsupervised method based on Generative Adversarial Networks (GAN) to classify scenarios as traversable or not based on visual data.
• Our method is inspired by the recent success of data-driven approaches on computer vision problems and anomaly detection, and reduces the need for vast amounts of negative examples at training time.