Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks

We present a new method to relocalize the 6DOF pose of an event camera solely based on the event stream. Our method first creates the event image from a list of events that occurs in a very short time interval, then a Stacked Spatial LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is composed of a CNN to learn deep features from the event images and a stack of LSTM to learn spatial dependencies in the image feature space. We show that the spatial dependency plays an important role in the relocalization task and the SP-LSTM can effectively learn this information. The experimental results on a publicly available dataset show that our approach generalizes well and outperforms recent methods by a substantial margin. Overall, our proposed method reduces by approx. 6 times the position error and 3 times the orientation error compared to the current state of the art. The source code and trained models will be released.Comment: 7 pages, 5 figure

Perilaku Guru dan Pegawai Sekolah terhadap Penerapan Program Zero Waste di Sekolah: Studi Kasus SMK Maitreyawira Batam

Zero waste (nol sampah) merupakan suatu konsep pengurangan produksi sampah. Konsep zero waste ini salah satunya dengan menerapkan prinsip 3 R (Reduce, Reuse, Recycle). Penerapan program zero waste dapat memberikan kontribusi dalam pemeliharaan lingkungan, yaitu dengan cara meminimalisir bahkan menghilangkan pembuangan sampah ke alam semesta dengan cara pengolahan kembali. Indonesia sendiri sudah mulai memperkenalkan konsep zero waste ini ke masyarakat, salah satunya melalui sekolah-sekolah. Penelitian ini bertujuan untuk mengetahui hubungan antara sikap dari guru dan pegawai sekolah terhadap penerapan program zero waste di sekolah. Pengumpulan data dilakukan melalui observasi, wawancara dan penyebaran kuisioner. Data-data yang terkumpul dianalisa menggunakan metode chi-square. Ada lima variabel independen, yaitu pengetahuan, sikap menerima, sikap merespon, sikap menghargai dan sikap bertanggungjawab, sedangkan variabel dependen adalah tindakan. Hasil penelitian menunjukkan bahwa hanya satu variabel yang memiliki hubungan yang signifikan terhadap penerapan zero waste di sekolah, yaitu sikap menerima

Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks

We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are then used to encode the visual features and sequentially generate the output words as the command. We demonstrate that the translation accuracy can be improved by allowing a smooth transaction between two RNN layers and using the state-of-the-art feature extractor. The experimental results on our new challenging dataset show that our approach outperforms recent methods by a fair margin. Furthermore, we combine the proposed translation module with the vision and planning system to let a robot perform various manipulation tasks. Finally, we demonstrate the effectiveness of our framework on a full-size humanoid robot WALK-MAN

THE ASSOCIATION OF OPTIMISM WITH TYPE 2 DIABETES BY AGE AMONG AFRICAN AMERICAN MEN AND WOMEN IN THE JACKSON HEART STUDY

African American adults are twice as likely to develop type 2 diabetes (T2D) compared toNon-Hispanic White individuals. Positive psychosocial resources (optimism) are associated withreduced cardiovascular disease (CVD) risk in African American individuals. However, littledata exist on the relationship between optimism and T2D among African American individualsby age. We hypothesize that high levels of optimism and younger individuals will be negativelyassociated with T2D. This study examines the association of optimism with T2D prevalenceamong African American men and women stratified by age categories, using data from theJackson Heart Study (JHS). Optimism was measured using the 6-item Life OrientationTest-Revised (LOT-R) scale. Using multivariable Poisson regression analysis, we estimated theprevalence ratios (PR, 95% confidence interval-CI) of diabetes (yes/no) by tertiles of optimism(low, medium, high), adjusting for age, sex, socioeconomic status, and behaviors. A greaterpercentage of participants with high optimism were younger, male, high SES, and not depressed(p<0.05). Optimism, as measured in standard deviation units, showed a significant inverserelationship between levels of optimism and diabetes (0.94 95% CI 0.90-0.99). The associationattenuated after full adjustment. High (vs. low) total optimism was more protective againstdiabetes for participants less than 45 years of age (PR 0.49, 95% CI 0.30-0.79) than olderparticipants after full adjustment. The results from this study demonstrate that interventions thatfocus on positive psychosocial well-being in Black adults may help to reduce the risk of diabetesin an aging population

Learning Task Priorities from Demonstrations

Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the Task-Parameterized Gaussian Mixture Model (TP-GMM) to Jacobian and null space structures. The proposed approach is tested on bimanual skills but can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: two different tasks with humanoids requiring the learning of priorities and a loco-manipulation scenario, showing that the approach can be exploited to learn the prioritization of multiple tasks in parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic

Geometry-aware Manipulability Learning, Tracking and Transfer

Body posture influences human and robots performance in manipulation tasks, as appropriate poses facilitate motion or force exertion along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control and design the robot dexterity as a function of the articulatory joint configuration. This descriptor can be designed according to different task requirements, such as tracking a desired position or apply a specific force. In this context, this paper presents a novel \emph{manipulability transfer} framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations. The proposed learning scheme is built on a tensor-based formulation of a Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive definite matrices. Learning is coupled with a geometry-aware tracking controller allowing robots to follow a desired profile of manipulability ellipsoids. Extensive evaluations in simulation with redundant manipulators, a robotic hand and humanoids agents, as well as an experiment with two real dual-arm systems validate the feasibility of the approach.Comment: Accepted for publication in the Intl. Journal of Robotics Research (IJRR). Website: https://sites.google.com/view/manipulability. Code: https://github.com/NoemieJaquier/Manipulability. 24 pages, 20 figures, 3 tables, 4 appendice