Benjamin Pitzer

Rosbridge: ROS for Non-ROS Users

We present rosbridge, a middleware abstraction layer which provides robotics technology with a standard, minimalist applications development framework accessible to applications programmers who are not themselves roboticists. Rosbridge provides a simple, socket-based programmatic access to robot interfaces and algorithms provided (for now) by ROS, the open-source “Robot Operating System”, the current state-of-the-art in robot middleware. In particular, it facilitates the use of web technologies such as Javascript for the purpose of broadening the use and usefulness of robotic technology. We demonstrate potential applications in the interface design, education, human-robot interaction and remote laboratory environments.

Semantic Object Maps for robotic housework - representation, acquisition and use

In this article we investigate the representation and acquisition of Semantic Objects Maps (SOMs) that can serve as information resources for autonomous service robots performing everyday manipulation tasks in kitchen environments. These maps provide the robot with information about its operation environment that enable it to perform fetch and place tasks more efficiently and reliably. To this end, the semantic object maps can answer queries such as the following ones: “What do parts of the kitchen look like?”, “How can a container be opened and closed?”, “Where do objects of daily use belong?”, “What is inside of cupboards/drawers?”, etc. The semantic object maps presented in this article, which we call SOM+, extend the first generation of SOMs presented by Rusu et al. [1] in that the representation of SOM+ is designed more thoroughly and that SOM+ also include knowledge about the appearance and articulation of furniture objects. Also, the acquisition methods for SOM+ substantially advance those developed in [1] in that SOM+ are acquired autonomously and with low-cost (Kinect) instead of very accurate (laser-based) 3D sensors. In addition, perception methods are more general and are demonstrated to work in different kitchen environments.

Lidar-based lane marker detection and mapping

The detection of lane markers is a pre-requisite for many driver assistance systems as well as for autonomous vehicles. In this paper, the lane marker detection approach that was developed by Team AnnieWAY for the DARPA Urban Challenge 2007 is described. Based on current sensor technology, a robust real-time lane marker detection was developed and implemented. The system allows the robust estimation of a deviations between a digital map and the real world.

Robots as web services: Reproducible experimentation and application development using rosjs

We describe our efforts to create infrastructure to enable web interfaces for robotics. Such interfaces will enable researchers and users to remotely access robots through the internet as well as expand the types of robotic applications available to users with web-enabled devices. This paper centers on rosjs, a lightweight Javascript binding for ROS, Willow Garages robot middleware framework. rosjs exposes many of the capabilities of ROS, allowing application developers to write controllers that are executed through a web browser. We discuss how rosjs extends ROS and briefly overview some of the features it provides. rosjs has been instrumental in the creation of remote laboratories featuring the iRobot Create and the PR2. These facilities will be available to the community as experimental resources. We describe the overall goals of this project as well as provide a brief description of how rosjs was used to help create web interfaces for these facilities.

Towards perceptual shared autonomy for robotic mobile manipulation

Reliability and availability are major concerns for autonomous systems. A personal robot has to solve complex tasks, such as loading a dishwasher or folding laundry, which are very difficult to automate robustly. In order for a robot to perform better in those applications, it needs to be capable of accepting help from a human operator.

Bimanual robotic cloth manipulation for laundry folding

The physical properties of highly deformable objects such as clothing poses a challenging problem for autonomously acting systems. Especially, grasping and manipulation require new approaches that can accommodate for an objects variable and changing appearance. In this paper, we present a system that is capable of fully autonomously transforming a clothing item from a random crumpled configuration into a folded state. We describe a method to compute valid grasp poses on the cloth which accounts for deformability. Our algorithm includes a novel fold detection and grasp generation strategy, which suggests grasp poses on cloth folds. Machine learning techniques are used to evaluate these grasp poses. In our experiments, we use a stock PR2 robot whose two arms alternatingly perform grasps on a T-shirt equipped with fiducial markers. The goal of this grasp sequence is to bring the T-shirt into a configuration from which the robot can fold it. In several experiments, we demonstrate the performance of our approach.

PR2 Remote Lab: An environment for remote development and experimentation

In this paper, we describe a remote lab system that allows remote groups to access a shared PR2. This lab will enable a larger and more diverse group of researchers to participate directly in state-of-the-art robotics research and will improve the reproducibility and comparability of robotics experiments. We identify a set of requirements that apply to all web-based remote laboratories and focus on solutions to these requirements. Specifically, we present solutions to interface, control and design difficulties in the client and server-side software when implementing a remote laboratory architecture. The combination of shared physical hardware and shared middleware software allows for experiments that build upon and compare against results on the same platform and in the same environment for common tasks. We describe how researchers can interact with the PR2 and its environment remotely through a web interface, as well as develop similar interfaces to visualize and run experiments remotely.

Manipulator state estimation with low cost accelerometers and gyroscopes

Robot manipulator designs are increasingly focused on low cost approaches, especially those envisioned for use in unstructured environments such as households, office spaces and hazardous environments. The cost of angular sensors varies based on the precision offered. For tasks in these environments, millimeter order manipulation errors are unlikely to cause drastic reduction in performance. In this paper, estimates the joint angles of a manipulator using low cost triaxial accelerometers by taking the difference between consecutive acceleration vectors. The accelerometer-based angle is compensated with a uniaxial gyroscope using a complementary filter to give robust measurements. Three compensation strategies are compared: complementary filter, time varying complementary filter, and extended Kalman filter. This sensor setup can also accurately track the joint angle even when the joint axis is parallel to gravity and the accelerometer data does not provide useful information. In order to analyze this strategy, accelerometers and gyroscopes were mounted on one arm of a PR2 robot. The arm was manually moved smoothly through different trajectories in its workspace while the joint angle readings from the on-board optical encoders were compared against the joint angle estimates from the accelerometers and gyroscopes. The low cost angle estimation strategy has a mean error 1.3° over the three joints estimated, resulting in mean end effector position errors of 6.1 mm or less. This system provides an effective angular measurement as an alternative to high precision encoders in low cost manipulators and as redundant measurements for safety in other manipulators.

Remote Robotic Laboratories for Learning from Demonstration Enabling user interaction and shared experimentation

This paper documents the technology developed during the creation of the PR2 Remote Lab and the process of using it for shared development for Learning from Demonstration. Remote labs enable a larger and more diverse group of researchers to participate directly in state-of-the-art robotics research and will improve the reproducibility and comparability of robotics experiments. We present solutions to interface, control, and design difficulties in the client and server-side software when implementing a remote laboratory architecture. We describe how researchers can interact with the PR2 and its environment remotely through a web interface, as well as develop similar interfaces to visualize and run experiments remotely.Additionally, we describe how the remote lab technology was used by researchers participating in the Robot Learning from Demonstration Challenge (LfD) held in conjunction with the AAAI-11 Conference on Artificial Intelligence. Teams from three institutions used the remote lab as their primary development and testing platform. This paper reviews the process as well as providing observations and lessons learned.

Team AnnieWAY’s Autonomous System for the DARPA Urban Challenge 2007

This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that has successfully entered the finals of the 2007 DARPA Urban Challenge competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. Environmental perception mainly relies on a recent laser scanner which delivers both range and reflectivity measurements. While range measurements are used to provide 3D scene geometry, measuring reflectivity allows for robust lane marker detection. Mission and maneuver planning is conducted using a concurrent hierarchical state machine that generates behavior in accordance with California traffic laws. We conclude with a report of the results achieved during the competition.