Deep Visual Foresight for Planning Robot Motion

A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision. We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation -- pushing objects -- and can handle novel objects not seen during training.Comment: ICRA 2017. Supplementary video: https://sites.google.com/site/robotforesight

Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects

We introduce a method based on the deflectometry principle for the reconstruction of specular objects exhibiting significant size and geometric complexity. A key feature of our approach is the deployment of an Automatic Virtual Environment (CAVE) as pattern generator. To unfold the full power of this extraordinary experimental setup, an optical encoding scheme is developed which accounts for the distinctive topology of the CAVE. Furthermore, we devise an algorithm for detecting the object of interest in raw deflectometric images. The segmented foreground is used for single-view reconstruction, the background for estimation of the camera pose, necessary for calibrating the sensor system. Experiments suggest a significant gain of coverage in single measurements compared to previous methods. To facilitate research on specular surface reconstruction, we will make our data set publicly available

Dynamic update of a virtual cell for programming and safe monitoring of an industrial robot

A hardware/software architecture for robot motion planning and on-line safe monitoring has been developed with the objective to assure high flexibility in production control, safety for workers and machinery, with user-friendly interface. The architecture, developed using Microsoft Robotics Developers Studio and implemented for a six-dof COMAU NS 12 robot, established a bidirectional communication between the robot controller and a virtual replica of the real robotic cell. The working space of the real robot can then be easily limited for safety reasons by inserting virtual objects (or sensors) in such a virtual environment. This paper investigates the possibility to achieve an automatic, dynamic update of the virtual cell by using a low cost depth sensor (i.e., a commercial Microsoft Kinect) to detect the presence of completely unknown objects, moving inside the real cell. The experimental tests show that the developed architecture is able to recognize variously shaped mobile objects inside the monitored area and let the robot stop before colliding with them, if the objects are not too small

Heterodyne range imaging as an alternative to photogrammetry

Solid-state full-field range imaging technology, capable of determining the distance to objects in a scene simultaneously for every pixel in an image, has recently achieved sub-millimeter distance measurement precision. With this level of precision, it is becoming practical to use this technology for high precision three-dimensional metrology applications. Compared to photogrammetry, range imaging has the advantages of requiring only one viewing angle, a relatively short measurement time, and simplistic fast data processing. In this paper we fist review the range imaging technology, then describe an experiment comparing both photogrammetric and range imaging measurements of a calibration block with attached retro-reflective targets. The results show that the range imaging approach exhibits errors of approximately 0.5 mm in-plane and almost 5 mm out-of-plane; however, these errors appear to be mostly systematic. We then proceed to examine the physical nature and characteristics of the image ranging technology and discuss the possible causes of these systematic errors. Also discussed is the potential for further system characterization and calibration to compensate for the range determination and other errors, which could possibly lead to three-dimensional measurement precision approaching that of photogrammetry