Simultaneous multi-view object recognition and grasping in open-ended domains

To aid humans in everyday tasks, robots need to know which objects exist in the scene, where they are, and how to grasp and manipulate them in different situations. Therefore, object recognition and grasping are two key functionalities for autonomous robots. Most state-of-the-art approaches treat object recognition and grasping as two separate problems, even though both use visual input. Furthermore, the knowledge of the robot is fixed after the training phase. In such cases, if the robot encounters new object categories, it must be retrained to incorporate new information without catastrophic forgetting. In order to resolve this problem, we propose a deep learning architecture with an augmented memory capacity to handle open-ended object recognition and grasping simultaneously. In particular, our approach takes multi-views of an object as input and jointly estimates pixel-wise grasp configuration as well as a deep scale- and rotation-invariant representation as output. The obtained representation is then used for open-ended object recognition through a meta-active learning technique. We demonstrate the ability of our approach to grasp never-seen-before objects and to rapidly learn new object categories using very few examples on-site in both simulation and real-world settings. A video of these experiments is available online at: this https UR

Visualizing Mechanics: Educational Videos Demonstrating Core Mechanics Concepts

There has been an increased interest in using technology to relay information over the past 20 years. This interest has been reflected in the way the internet has expanded to incorporate nearly-uncountable numbers of blogs, streaming videos, and discussion forums. Recent research has shown that younger generations of students prefer internet-based platforms such as these to communicate and learn, as they are congruent with a visual style of learning. The present study seeks to fuse the predominantly reflective and intuitive methods found in a traditional lecture with the more visual and active modes of learning to provide a larger range of learning opportunities. Specifically, the authors seek to create educational videos in the technical area of mechanics that are appealing to students, and useful in their studies. These videos encompass experimental activities and mathematical analyses associated with core mechanics topics. The videos provide necessary background information and a brief overview of the experiment, and then proceed with a demonstration and subsequent analysis. This format allows the students to fully grasp the topic of interest, and in many cases ties it to a real-life example. The authors believe that ultimately these videos will enhance student understanding by appealing to a variety of learning styles, and ultimately have a marked impact on pertinent student success metrics