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.

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.

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.

Building robots capable of long term autonomy has been a long standing goal of robotics research. Such systems must be capable of performing certain tasks with a high degree of robustness and repeatability. In the context of personal robotics, these tasks could range anywhere from retrieving items from a refrigerator, loading a dishwasher, to setting up a dinner table. Given the complexity of tasks there are a multitude of failure scenarios that the robot can encounter, irrespective of whether the environment is static or dynamic. For a robot to be successful in such situations, it would need to know how to recover from failures or when to ask a human for help. This paper, presents a novel shared autonomy behavioral executive to addresses these issues. We demonstrate how this executive combines generalized logic based recovery and human intervention to achieve continuous failure free operation. We tested the systems over 250 trials of two different use case experiments. Our current algorithm drastically reduced human intervention from 26% to 4% on the first experiment and 46% to 9% on the second experiment. This system provides a new dimension to robot autonomy, where robots can exhibit long term failure free operation with minimal human supervision. We also discuss how the system can be generalized.

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 Garage's 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.

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. Shared autonomy is a system model based on human-robot dialogue. This work aims at bridging the gap between full human control and full autonomy for tasks in the domain of personal robotics. One of the hardest problems for personal robotic systems is perception: perceiving and inferring about objects in the robot's environment. We present a system capable of solving the perceptual inference in combination with a human, such that a human operator functions as a resource for the robot and helps to compensate for limitations of autonomy. In this paper, we show how a human-robot team can work together effectively to solve complex perception tasks. We present a system that asks a human operator to identify objects it doesn't recognize or find. In various experiments with the PR2 robot we show that this shared autonomy system performs more robustly than the robot system alone and that it is capable of tasks which are difficult to accomplish by an autonomous agent.

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 object's 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.

This paper describes a system for automatic mapping and generation of textured 3D models of indoor environments without user interaction. Our data acquisition system is based on a Segway RMP platform which allows us to automatically acquire large amounts of textured 3D scans in a short amount of time. The first data processing step is registration and mapping. We propose a probabilistic, non-rigid registration method that incorporates statistical sensor models and surface prior distributions to optimize alignment and the reconstructed surface at the same time. Second, in order to fuse multiple scans and to reconstruct a consistent 3D surface representation, we incorporate a volumetric surface reconstruction method based on a oriented point. For the final step of texture reconstruction, we present a novel method to automatically generate blended textures from multiple images and multiple scans which are mapped onto the 3D model for photo-realistic visualization. We conclude our report with results from a large-scale, real-world experiment. The most significant contribution of this research is a functional system that covers all steps required to automatically reconstruct textured 3D models of large indoor environments.

Generating accurate environment representations can significantly improve the autonomy of mobile robots. In this article we present a novel probabilistic technique for solving the full SLAM problem by jointly solving the data registration problem and the accurate reconstruction of the underlying geometry. The key idea of this paper is to incorporate spatial correlation models as prior knowledge on the map we seek to construct. We formulate the mapping problem as a maximum a-posteriori estimation comprising common probabilistic motion and sensor models as well as two spatial correlation models to guide the optimization. Instead of discarding data at an early stage, our algorithm makes use of all data available in the optimization process. When applied to SLAM, our method generates maps that closely resemble the real environment. We compare our approach to state-of-the-art algorithms, using both real and synthetic data sets.