Using a Machine Learning Approach to Implement and Evaluate Product Line Features

Bike-sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility. In this paper we are interested in studying and modeling the behavior of features that permit the end user to access, with her/his web browser, the status of the Bike-Sharing system. In particular, we address features able to make a prediction on the system state. We propose to use a machine learning approach to analyze usage patterns and learn computational models of such features from logs of system usage. On the one hand, machine learning methodologies provide a powerful and general means to implement a wide choice of predictive features. On the other hand, trained machine learning models are provided with a measure of predictive performance that can be used as a metric to assess the cost-performance trade-off of the feature. This provides a principled way to assess the runtime behavior of different components before putting them into operation.Comment: In Proceedings WWV 2015, arXiv:1508.0338

A Factor Graph Approach to Multi-Camera Extrinsic Calibration on Legged Robots

Legged robots are becoming popular not only in research, but also in industry, where they can demonstrate their superiority over wheeled machines in a variety of applications. Either when acting as mobile manipulators or just as all-terrain ground vehicles, these machines need to precisely track the desired base and end-effector trajectories, perform Simultaneous Localization and Mapping (SLAM), and move in challenging environments, all while keeping balance. A crucial aspect for these tasks is that all onboard sensors must be properly calibrated and synchronized to provide consistent signals for all the software modules they feed. In this paper, we focus on the problem of calibrating the relative pose between a set of cameras and the base link of a quadruped robot. This pose is fundamental to successfully perform sensor fusion, state estimation, mapping, and any other task requiring visual feedback. To solve this problem, we propose an approach based on factor graphs that jointly optimizes the mutual position of the cameras and the robot base using kinematics and fiducial markers. We also quantitatively compare its performance with other state-of-the-art methods on the hydraulic quadruped robot HyQ. The proposed approach is simple, modular, and independent from external devices other than the fiducial marker.Comment: To appear on "The Third IEEE International Conference on Robotic Computing (IEEE IRC 2019)

PENINGKATAN KRETIVITAS, MOTIVASI, DAN PRESTASI BELAJAR IPS MENGGUNAKAN MODEL PEMBEAJARAN TEAM GAMES TOURNAMENT

The aim of this research is to improve creativity, motivation, and social sciences students achievement in the fourth grade students of SDN Tunggorono in academic year 2015/2016 using model of learning Team Games Tournament. The research subjects were 22 fourth grade students of SDN Tunggorono. The research prosedur starting from planning, observation, and reflektion. This research consist of two cycles. The data collection technique are using observation, test, interview, and documentation. The data of creativity, motivation assessed from observation sheet and observed during the learning process. Data analysis technique using percentage (quantitative) and qualitative description. The susses indicator if 75% of student have showing their creativity, very good motivation, and interpretation. The result showed the creativity of pre test cycle to 40% less than I cycle increased to 72,13% and II cycle to 78,43%. The learning motivation has increased from pre cycle the average percentage of 50% in the first cycle increased to 73,38% and in II cycle to 78,88%. Learning achievement of social science also increased, from pre cycle of in students (63,64%) has not completed study, students (36,36) has completed study. KKM to set for social science in I cycle 50% (II student) has completed study and in II cycle 81,82 (18 students) has completed study. The research have success in II cycle

Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimation

Many algorithms for control, optimization and estimation in robotics depend on derivatives of the underlying system dynamics, e.g. to compute linearizations, sensitivities or gradient directions. However, we show that when dealing with Rigid Body Dynamics, these derivatives are difficult to derive analytically and to implement efficiently. To overcome this issue, we extend the modelling tool `RobCoGen' to be compatible with Automatic Differentiation. Additionally, we propose how to automatically obtain the derivatives and generate highly efficient source code. We highlight the flexibility and performance of the approach in two application examples. First, we show a Trajectory Optimization example for the quadrupedal robot HyQ, which employs auto-differentiation on the dynamics including a contact model. Second, we present a hardware experiment in which a 6 DoF robotic arm avoids a randomly moving obstacle in a go-to task by fast, dynamic replanning