Papers for

outdoor robotics engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Robot terrain recognition improves with attention and new surface classes

Attention-Based Surface Representation Learning for Robot State Prediction and Open-Ended Surface Classification

Abstract: For ground robots operating in outdoor environments, understanding the properties of the underlying terrain is essential for ensuring reliable operation. In most perception-based studies, this problem is formulated as categorical classification with a fixed number of classes defined during training. We propose an approach that enables new surface classes to be added as trainable vectors, which can subsequently be used to address higher-level tasks. By employing a learning paradigm based on predicting the robot's next state in time and using attention blocks, we improved classification accuracy to 98.56% on the Belyaev-Kushnarev dataset and 94.8% on BorealTC.

Sat 3 OctRoboticsArtificial IntelligenceMachine Learning

The gist

For robots that move outside, knowing what kind of ground they are on helps them work better. Most methods only recognize a fixed set of ground types known beforehand. The authors created a way for robots to learn new types of ground surfaces as they encounter them. Their method uses a technique called attention and predicts the robot’s future movements to better classify surfaces. They tested this on two datasets and got very high accuracy.

Open → 2610.04240v1