This paper gives an overview of ROS, an opensource robot operating system. ROS is not an operating system in the traditional sense of process management and scheduling; rather, it provides a structured communications layer above the host operating systems of a heterogenous compute cluster. In this paper, we discuss how ROS relates to existing robot software frameworks, and briefly overview some of the available application software which uses ROS.

ROS: an open-source Robot Operating System

Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.

/pdf/end-to-end-training-of-deep-visuomotor-policies-1vqd5uo50d.pdf

End-to-end training of deep visuomotor policies

Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a partially observed guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.

End-to-End Training of Deep Visuomotor Policies

Society is becoming more automated with robots beginning to perform most tasks in factories and starting to help out in home and office environments. One of the most important functions of robots is the ability to manipulate objects in their environment. Because the space of possible robot designs, sensor modalities, and target tasks is huge, researchers end up having to manually create many models, databases, and programs for their specific task, an effort that is repeated whenever the task changes. Given a specification for a robot and a task, the presented framework automatically constructs the necessary databases and programs required for the robot to reliably execute manipulation tasks. It includes contributions in three major components that are critical for manipulation tasks. 
The first is a geometric-based planning system that analyzes all necessary modalities of manipulation planning and offers efficient algorithms to formulate and solve them. This allows identification of the necessary information needed from the task and robot specifications. Using this set of analyses, we build a planning knowledge-base that allows informative geometric reasoning about the structure of the scene and the robot's goals. We show how to efficiently generate and query the information for planners. 
The second is a set of efficient algorithms considering the visibility of objects in cameras when choosing manipulation goals. We show results with several robot platforms using grippers cameras to boost accuracy of the detected objects and to reliably complete the tasks. Furthermore, we use the presented planning and visibility infrastructure to develop a completely automated extrinsic camera calibration method and a method for detecting insufficient calibration data. 
The third is a vision-centric database that can analyze a rigid object's surface for stable and discriminable features to be used in pose extraction programs. Furthermore, we show work towards a new voting-based object pose extraction algorithm that does not rely on 2D/3D feature correspondences and thus reduces the early-commitment problem plaguing the generality of traditional vision-based pose extraction algorithms. 
In order to reinforce our theoric contributions with a solid implementation basis, we discuss the open-source planning environment OpenRAVE, which began and evolved as a result of the work done in this thesis. We present an analysis of its architecture and provide insight for successful robotics software environments.

Automated construction of robotic manipulation programs

We present a novel vision-based grasp point detection algorithm that can reliably detect the corners of a piece of cloth, using only geometric cues that are robust to variation in texture. Furthermore, we demonstrate the effectiveness of our algorithm in the context of folding a towel using a general-purpose two-armed mobile robotic platform without the use of specialized end-effectors or tools. The robot begins by picking up a randomly dropped towel from a table, goes through a sequence of vision-based re-grasps and manipulations—partially in the air, partially on the table—and finally stacks the folded towel in a target location. The reliability and robustness of our algorithm enables for the first time a robot with general purpose manipulators to reliably and fully-autonomously fold previously unseen towels, demonstrating success on all 50 out of 50 single-towel trials as well as on a pile of 5 towels.

/pdf/cloth-grasp-point-detection-based-on-multiple-view-geometric-1karf8krbt.pdf

Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding

The most critical challenge for Personal Robotics is to manage the issue of human safety and yet provide the physical capability to perform useful work. This paper describes a novel concept for a mobile, 2-armed, 25-degree-of- freedom system with backdrivable joints, low mechanical impedance, and a 5 kg payload per arm. System identification, design safety calculations and performance evaluation studies of the first prototype are included, as well as plans for a future development.

Towards a personal robotics development platform: Rationale and design of an intrinsically safe personal robot

Systems and methods related to construction, configuration, and utilization of humanoid robotic systems and aspects thereof are described. A system may include a mobile base, a spine structure, a body structure, and at least one robotic arm, each of which is movably configured to have significant human-scale capabilities in prescribed environments. The one or more robotic arms may be rotatably coupled to the body structure, which may be mechanically associated with the mobile base and spine such that it may be deflectably elevated and rolled relative to the base simultaneously and independently. Aspects of the one or more arms may be counterbalanced with one or more spring-based counterbalancing mechanisms which facilitate backdriveability and payload features.

Electromechanically counterbalanced humanoid robotic system

An actuation system for a multi-segmented robot linkage is provided. The system includes (i) a gravity counter balancing mechanism for the multi-segmented robot linkage and a payload in contact with the multi-segmented robot linkage and (ii) a plurality of actuators acting on the joints of the multi-segmented robot linkage, whereby the actuators are high-bandwidth back-drivable actuators.

Keenan A. Wyrobek

Papers

Towards a personal robotics development platform: Rationale and design of an intrinsically safe personal robot

Electromechanically counterbalanced humanoid robotic system

Spring-based vectoring system for robotic aplications