In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot ...

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Recent Advances in Robot Learning from Demonstration

We describe CST, an online algorithm for constructing skill trees from demonstration trajectories. CST segments a demonstration trajectory into a chain of component skills, where each skill has a goal and is assigned a suitable abstraction from an abstraction library. These properties permit skills to be improved efficiently using a policy learning algorithm. Chains from multiple demonstration trajectories are merged into a skill tree. We show that CST can be used to acquire skills from human demonstration in a dynamic continuous domain, and from both expert demonstration and learned control sequences on the uBot-5 mobile manipulator.

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Robot learning from demonstration by constructing skill trees

In this paper, we present the design and implementation of Rapyuta, an open-source cloud robotics platform. Rapyuta helps robots to offload heavy computation by providing secured customizable computing environments in the cloud. The computing environments also allow the robots to easily access the RoboEarth knowledge repository. Furthermore, these computing environments are tightly interconnected, paving the way for deployment of robotic teams. We also describe three typical use cases, some benchmarking and performance results, and two proof-of-concept demonstrations.

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Rapyuta: A Cloud Robotics Platform

Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks Additionally, we have recently seen a focus on gathering data from non-expert human teachers (ie, domain experts but not robotics experts) In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations Next, is the choice of modeling technique Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions We devote a chapter to each of these Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain

Robot Learning from Human Teachers

Robots exhibit flexible behavior largely in proportion to their degree of knowledge about the world. Such knowledge is often meticulously hand-coded for a narrow class of tasks, limiting the scope of possible robot competencies. Thus, the primary limiting factor of robot capabilities is often not the physical attributes of the robot, but the limited time and skill of expert programmers. One way to deal with the vast number of situations and environments that robots face outside the laboratory is to provide users with simple methods for programming robots that do not require the skill of an expert. For this reason, learning from demonstration (LfD) has become a popular alternative to traditional robot programming methods, aiming to provide a natural mechanism for quickly teaching robots. By simply showing a robot how to perform a task, users can easily demonstrate new tasks as needed, without any special knowledge about the robot. Unfortunately, LfD often yields little knowledge about the world, and thus lacks robust generalization capabilities, especially for complex, multi-step tasks. We present a series of algorithms that draw from recent advances in Bayesian non-parametric statistics and control theory to automatically detect and leverage repeated structure at multiple levels of abstraction in demonstration data. The discovery of repeated structure provides critical insights into task invariants, features of importance, high-level task structure, and appropriate skills for the task. This culminates in the discovery of a finite-state representation of the task, composed of grounded skills that are flexible and reusable, providing robust generalization and transfer in complex, multi-step robotic tasks. These algorithms are tested and evaluated using a PR2 mobile manipulator, showing success on several complex real-world tasks, such as furniture assembly.

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Learning grounded finite-state representations from unstructured demonstrations

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.

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Rosbridge: ROS for non-ROS users

We present a study of using a robotic learning from demonstration system capable of collecting large amounts of human-robot interaction data through a web-based interface. We examine the effect of different perceptual mappings between the human teacher and robot on the learning from demonstration. We show that humans are significantly more effective at teaching a robot to navigate a maze when presented with information that is limited to the robot's perception of the world, even though their task performance measurably suffers when contrasted with users provided with a natural and detailed raw video feed. Robots trained on such demonstrations learn more quickly, perform more accurately and generalize better. We also demonstrate a set of software tools for enabling internet-mediated human-robot interaction and gathering the large datasets that such crowdsourcing makes possible.

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Human and robot perception in large-scale learning from demonstration

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.

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

The advent of ROS, the Robot Operating System, has finally made it possible to implement and use state-of-the-art navigation and manipulation algorithms on widely-available, inexpensive standard robot platforms. With the addition of the Rosbridge application programming interface, interface designers and applications programmers can create robot interfaces and behaviors without venturing into the specialized world of robotics engineers. This tutorial introduces ROS and Rosbridge, and shows how quickly and easily these tools can be used to design and conduct large-scale online HRI experiments, access algorithms for autonomous robot behavior, and leverage the huge ecosystem of general-purpose web-based and application-oriented software engineering for robotics and HRI research. Tutorial attendees will learn the basics of autonomous and teleoperated navigation and manipulation, as well as interface design for online interaction with robots. During the tutorial they will design and write their own remote presence application, as well as develop strategies for incorporating autonomy and dealing with data collection.

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ROS and Rosbridge: roboticists out of the loop

Using data collected from human teleoperation, our goal is to learn a control policy that maps perception to actuation. Such policies are potentially multi-valued with regard to perception with a single input mapping to multiple outputs depending on the user's objective at a particular time. We propose a multi-valued function regressor to learn a larger class of robot control policies from human demonstration and extend the Hierarchical Dirichlet Process Hidden Markov Model to discover latent variables representing unknown objectives in the demonstrated data and the transitions between these objectives. Each of these objectives requires only a single-valued policy function, and thus can be learned with a Gaussian process function regressor. The learned transitions between these objectives determine the correct actuation where the complete policy function is multi-valued. We present the results of experiments conducted on the Nao humanoid robot platform.

Graylin Jay

Papers

Rosbridge: ROS for non-ROS users

Human and robot perception in large-scale learning from demonstration

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

ROS and Rosbridge: roboticists out of the loop

Learning from demonstration using a multi-valued function regressor for time-series data