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Learning and memory in honeybees is analyzed on five levels, using a top-down approach. (a) Observatory learning is applied during navigation and dance communication. (b) Local cues at the feeding site are learned associatively. (c) Classical conditioning of the proboscis extension response to olfactory stimuli provides insight into behavioral, neural, and(More)
We describe a vision-based obstacle avoidance system for off-road mobile robots. The system is trained from end to end to map raw input images to steering angles. It is trained in supervised mode to predict the steering angles provided by a human driver during training runs collected in a wide variety of terrains, weather conditions, lighting conditions,(More)
— Recent advances in self-supervised learning have enabled very long-range visual detection of obstacles and pathways (to 100 hundred meters or more). Unfortunately, the category and range of regions at such large distances come with a considerable amount of uncertainty. We present a mapping and planning system that accurately represents range and category(More)
Most vision-based approaches to mobile robotics suffer from the limitations imposed by stereo obstacle detection, which is short-range and prone to failure. We present a self-supervised learning process for long-range vision that is able to accurately classify complex terrain at distances up to the horizon, thus allowing superior strategic planning. The(More)
Voicemail is a pervasive, but under-researched tool for workplace communication. Despite potential advantages of voicemail over email, current phone-based voicemail UIs are highly problematic for users. We present a novel, Web-based, voicemail interface, Jotmail. The design was based on data from several studies of voicemail tasks and user strategies. The(More)
— We present a learning-based approach for long-range vision that is able to accurately classify complex terrain at distances up to the horizon, thus allowing high-level strategic planning. A deep belief network is trained with unsupervised data and a reconstruction criterion to extract features from an input image, and the features are used to train a(More)
We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with(More)
The physiological response to trauma-related stimuli of up to one third of participants with posttraumatic stress disorder (PTSD) cannot be discriminated from that of controls. Psychophysiological measures (heart rate and blood pressure) of 22 PTSD and 23 control civilian participants, all exposed to missile attacks during the Gulf War, were recorded while(More)
— We present a solution to the problem of long-range obstacle/path recognition in autonomous robots. The system uses sparse traversability information from a stereo module to train a classifier online. The trained classifier can then predict the traversability of the entire scene. A distance-normalized image pyramid makes it possible to efficiently train on(More)
— A novel probabilistic online learning framework for autonomous off-road robot navigation is proposed. The system is purely vision-based and is particularly designed for predicting traversability in unknown or rapidly changing environments. It uses self-supervised learning to quickly adapt to novel terrains after processing a small number of frames, and it(More)