8 accepted papers
We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. However, in robotics applications, it is often the case that (potentially very few) examples of anomalies ar
We introduce an approach to train neural network models for visual object localization using a small training set, labeled with ground truth object positions and a large unlabeled one. We assume that the object to be localized emits sound, which is perceived by a microphone rigidly affixed to the ca
When learning models for real-world robot spatial perception tasks, one might have access only to partial labels: this occurs for example in semi-supervised scenarios (in which labels are not available for a subset of the training instances) or in some types of self-supervised robot learning (where
We propose a general self-supervised learning approach for spatial perception tasks, such as estimating the pose of an object relative to the robot, from onboard sensor readings. The model is learned from training episodes, by relying on: A continuous state estimate, possibly inaccurate and affected
We introduce a novel approach to long-range path planning that relies on a learned model to predict the outcome of local motions using possibly partial knowledge. The model is trained from a dataset of trajectories acquired in a self-supervised way. Sampling-based path planners use this component to
We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor (such as a camera). We assume that the former is directly related to some piece of information to be perceived (such as
Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image represent
We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; we propose a