Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world. We propose a simpler learning-from-demonstration approach that is able to detect the object to grasp from merely a single demonstration using a convolutional neural network we call GraspNet. In order to increase robustness and decrease the training time even further, we leverage data from previous demonstrations to quickly fine-tune a GrapNet for each new demonstration. We present some preliminary results on a grasping experiment with the Franka Panda cobot for which we can train a GraspNet with only hundreds of train iterations.
The Indian Energy grid is growing rapidly and there is a large simulation to improve not only the grid reliability, but also provide power for all by 2027. To this aim the Government of India has launched the Restructured Accelerated Power Development Program (RAPDRP). In India, residential loads contribute to about 27% of the total power consumption, and hence understanding the residential power consumption is a key stepping stone to improving the performance of the grid and optimizing energy usage via dynamic load management like demand response programs. While there is a lot of prior-art related to short and long term forecasting residential loads, similar models are still very limited in the Indian context. The Indian power grid is a complex eco-system, where demand and consumption patterns are influenced by weather, socio-economic status, geographic location, political events to name a few. Hence, it is difficult to build a model for energy forecasting at residential level. This paper is an attempt to build a residential forecasting model depending on the socio-economic factor and geographical location. To this aim, in this paper we first discuss the influence of socio-economic status on the energy consumption in urban and rural area’s. The socio-economic status includes number of earning members of the family, age, gender and profession etc. All these factors influence the number of devices within a home, which in turn impacts the overall energy consumption. Based on some of these observations, recommendation on modeling approaches are made to enable future data driven research into the energy consumption at residential level in India. This can further be used to enable smart grid technologies, especially from a smart demand, response perspective.
In this paper we discuss some issues in the experimental evaluation of intelligent autonomous systems, focusing on systems, like autonomous robots, operating in physical environments. We argue that one of the weaknesses of current experimental practices is the low degree of generalization of experimental results, meaning that knowing the performance a robot system obtains in a test setting does not provide much information about the performance the same system could achieve in other settings. We claim that one of the main obstacles to achieve generalization of experimental results in autonomous robotics is the low degree of representativeness of the selected experimental settings. We survey and discuss the degree of representativeness of experimental settings used in a significant sample of current research and we propose some strategies to overcome the emerging limitations.
Detection and tracking of moving objects with camera systems mounted on a mobile robot presents a formidable problem since the ego-motion of the robot and the moving objects jointly form a challengingly discernible motion in the image. In this paper, we are concerned with multiple-camera systems, namely the Ladybug\(^{\textregistered }2\) camera, whose perspective images were used to detect motion and subsequently perform the tracking of multiple objects on the sphere. This enabled us to account for the continuity of the scene which is achieved by the sensor in an image stitching process on the sphere. The objects are tracked on the sphere with a Bayesian filter based on the von Mises–Fisher distribution and the data association is achieved by the global nearest neighbor method, for which the distance matrix is constructed by deriving the Rényi \(\alpha \)-divergence for the von Mises–Fisher distribution. The prospects of the method are tested on a synthetic and real-world data experiments.
