Learning to Grasp Arbitrary Household Objects from a Single Demonstration Upon the advent of Industry 4.0, collaborative robotics and intelligent automation gain more and more traction for enterprises to improve their production processes. In order to adapt to this trend, new programming, learning and collaborative techniques are investigated. Programbydemonstration is one of the techniques that aim to reduce the burden of manually programming collaborative robots. However, this is often limited to teaching to grasp at a certain position, rather than grasping a certain object. In this paper, we propose a method that learns to grasp an arbitrary object from visual input. While other learningbased approaches for robotic grasping require collecting a large dataset, manually or automatically labeled in a real or simulated world, our approach requires a single demonstration.We present results on grasping various objects with the Franka Panda collaborative robot after capturing a single image from a wrist mounted RGB camera. From this image we learn a robot controller with a convolutional neural network to adapt to changes in the objectxe2x80x99s position and rotation with less than 5 minutes of training time on a NVIDIA Titan X GPU, achieving over 90% grasp success rate.

Synthesizing Robot Manipulation Programs from a Single Observed Human Demonstration Programming by Demonstration (PbD) lets users with little technical background program a wide variety of manipulation tasks for robots, but it should be as intuitive as possible for users while requiring as little time as possible. In this paper, we present a Programming by Demonstration system that synthesizes manipulation programs from a single observed demonstration, allowing users to program new tasks for a robot simply by performing the task once themselves. A humanintheloop interface helps users make corrections to the perceptual state as needed. We introduce Object Interaction Programs as a representation of multiobject, bimanual manipulation tasks and present algorithms for extracting programs from observed demonstrations and transferring programs to a robot to perform the task in a new scene. We demonstrate the expressivity and generalizability of our approach through an evaluation on a benchmark of complex tasks.