210,268 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
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image synthesis, and is a departure from the hand-modeled virtual
worlds and approximate image synthesis methods used in real-time applications.
The benefits of our approach include flexible, physically accurate and scalable
image synthesis, implicit wide coverage of classes and features, and complete
data introspection for annotations, which all contribute to quality and cost
efficiency. To evaluate our approach and the efficacy of the resulting data, we
use semantic segmentation for autonomous vehicles and robotic navigation as the
main application, and we train multiple deep learning architectures using
synthetic data with and without fine tuning on organic (i.e. real-world) data.
The evaluation shows that our approach improves the neural network's
performance and that even modest implementation efforts produce
state-of-the-art results.Comment: The project web page at
http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the
paper with high-resolution images as well as additional materia
Kecelaruan personaliti antisosial di kalangan pelajar politeknik : satu kajian awal
Kajian ini adalah bertujuan untuk mengenalpasti kecelaruan personalis antisosial (KPA) yang berlaku di kalangan remaja atau muda-mudi terutama di Politeknik Malaysia yang mungkin mengakibatkan berlakunya masalah sosial di kalangan mereka. Kajian ini berbentuk kuantitatif. Sampel kajian telah dipilih di empat buah politeknik. Politeknik yang terlibat adalah politeknik zon selatan. Responden kajian ini terdiri daripada 340 orang pelajar pengambilan bam semester satu yang memasuki institusi
berkenaan. Responden juga terdiri daripada pelajar peringkat sijil dan diploma daripada pelbagai pengkhususan. Instrumen yang digunakan adalah borang soal selidik. Data yang telah dikumpulkan dianalisis menggunakan Statistical Package for Social Science (SPSS). Statistik yang digunakan adalah statistik deskriptif. Dapatan kajian menunjukkan di antara 10 jenis kecelaruan, kecelaruan avoidant mencatatkan skor min
tertinggi iaitu dengan skor min 3.24 (a = 1.055). Selain itu, pengkaji mendapati personaliti antisosial yang berlaku di kalangan pelajar politeknik adalah pada tahap yang sederhana iaitu skor min 2.35 (a =0.933). Hasil daripada kajian juga mendapati faktor sosial mencatatkan skor min tertinggi iaitu 2.07 (a = 0.851). Faktor keluarga pula hanya mencatatkan skor min 2.03 (g = 0.887). Pengkaji juga mendapati responden lebih gemar
kepada konsep keagamaan berbanding konsep-konsep yang lain sekiranya mereka menghadapi masalah. Oleh itu diharapkan kajian ini dapat memberikan penjelasan sedikit sebanyak mengenai kecelaruan personaliti antisosial yang berlaku di kalangan pelajar politeknik di masa kini
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