Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping

Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which ground-truth annotations are generated automatically. Unfortunately, models trained purely on simulated data often fail to generalize to the real world. We study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images. We extensively evaluate our approaches with a total of more than 25,000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN. We show that, by using synthetic data and domain adaptation, we are able to reduce the number of real-world samples needed to achieve a given level of performance by up to 50 times, using only randomly generated simulated objects. We also show that by using only unlabeled real-world data and our GraspGAN methodology, we obtain real-world grasping performance without any real-world labels that is similar to that achieved with 939,777 labeled real-world samples.Comment: 9 pages, 5 figures, 3 table

Pengamanan Data Teks Dengan NTRU Dan Modulus Function Pada Koefisien IHWT Citra Warna

The development of digital information have caused the rise of information technology security to protect text data that contains secret. Steganography is one of many solutions for securing text data by hiding the text data on an image so that another party would not know the existence of such data. Criteria of a good steganography involves imperceptibility, fidelity, robustness dan recovery. One steganographic method is CD (Coefficient Difference), adopted from PVD (Pixel Value Differencing) which does hiding in spatial domain using difference of two pixel values that results in large modification of pixel values, reducing imperceptibility. Modulus function is used to solve such shortcoming in CD by using the modulus function on embedding, minimizing pixel modification during the process, resulting in improved imperceptibility. In this final project, IHWT (Integer Haar Wavelet Transform) are used to keep imperceptibility high. To improve the security, cryptographic method NTRU is applied on the secret message before it is hidden in image. The result showed that the combination of NTRU, IHWT and modulus function yields good imperceptibility, with PSNR value above 40 dB while the stego image resist salt and pepper noise attack of 0,002% and contrast addition of maximum amount on