14,317 research outputs found
Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain
In this paper we tackle the problem of visually predicting surface friction
for environments with diverse surfaces, and integrating this knowledge into
biped robot locomotion planning. The problem is essential for autonomous robot
locomotion since diverse surfaces with varying friction abound in the real
world, from wood to ceramic tiles, grass or ice, which may cause difficulties
or huge energy costs for robot locomotion if not considered. We propose to
estimate friction and its uncertainty from visual estimation of material
classes using convolutional neural networks, together with probability
distribution functions of friction associated with each material. We then
robustly integrate the friction predictions into a hierarchical (footstep and
full-body) planning method using chance constraints, and optimize the same
trajectory costs at both levels of the planning method for consistency. Our
solution achieves fully autonomous perception and locomotion on slippery
terrain, which considers not only friction and its uncertainty, but also
collision, stability and trajectory cost. We show promising friction prediction
results in real pictures of outdoor scenarios, and planning experiments on a
real robot facing surfaces with different friction
How Does Our Visual System Achieve Shift and Size Invariance?
The question of shift and size invariance in the primate
visual system is discussed. After a short review of the relevant neurobiology and psychophysics, a more detailed analysis of computational models is given. The two main types of networks considered are the dynamic routing circuit model and invariant feature networks, such as the neocognitron. Some specific open questions in context of these models are raised and possible solutions discussed
Deep Learning: Our Miraculous Year 1990-1991
In 2020, we will celebrate that many of the basic ideas behind the deep
learning revolution were published three decades ago within fewer than 12
months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich.
Back then, few people were interested, but a quarter century later, neural
networks based on these ideas were on over 3 billion devices such as
smartphones, and used many billions of times per day, consuming a significant
fraction of the world's compute.Comment: 37 pages, 188 references, based on work of 4 Oct 201
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