We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically, we analyze and categorize the multiple ways in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques for policy derivation, including matching functions, dynamics models and plans. To conclude we discuss LfD limitations and related promising areas for future research.

A general learning task for a robot in a new environment is to learn about objects and what actions/effects they afford. To approach this, we look at ways that a human partner can intuitively help the robot learn, Socially Guided Machine Learning. We present experiments conducted with our robot, Junior, and make six observations characterizing how people approached teaching about objects. We show that Junior successfully used transparency to mitigate errors. Finally, we present the impact of “social ” versus “nonsocial” data sets when training SVM classifiers.

Abstract — We present a probabilistic architecture for solving generically the problem of extracting the task constraints through a Programming by Demonstration (PbD) framework and for generalizing the acquired knowledge to various situations. In previous work, we proposed an approach based on Gaussian Mixture Regression (GMR) to find a controller for the robot reproducing the essential characteristics of a skill in joint space and in task space through Lagrange optimization. In this paper, we extend this approach to a more generic procedure handling simultaneously constraints in joint space and in task space by combining directly the probabilistic representation of the task constraints with a simple Jacobian-based inverse kinematics solution. Experiments with two 5-DOFs Katana robots are presented with manipulation tasks that consist of handling and displacing a set of objects. I.

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Abstract — A robot can perform a given task through a policy that maps its sensed state to appropriate actions. We assume that a hand-coded controller can achieve such a mapping only for the basic cases of the task. Refining the controller becomes harder and gets more tedious and error prone as the complexity of the task increases. In this paper, we present a new learning from demonstration approach to improve the robot’s performance through the use of corrective human feedback as a complement to an existing hand-coded algorithm. The human teacher observes the robot as it performs the task using the hand-coded algorithm and takes over the control to correct the behavior when the robot selects a wrong action to be executed. Corrections are captured as new state-action pairs and the default controller output is replaced by the demonstrated corrections during autonomous execution when the current state of the robot is decided to be similar to a previously corrected state in the correction database. The proposed approach is applied to a complex ball dribbling task performed against stationary defender robots in a robot soccer scenario, where physical Aldebaran Nao humanoid robots are used. The results of our experiments show an improvement in the robot’s performance when the default hand-coded controller is augmented with corrective human demonstration. I.

Robot programming by demonstration (PbD) has become a central topic of robotics that spans across general research areas such as humanrobot interaction, machine learning, machine vision and motor control. Robot PbD started about 30 years ago, and has grown importantly during the past decade. The rationale for moving from purely preprogrammed robots to very flexible user-based interfaces for training robots to perform a task is three-fold. First and foremost, PbD, also referred to as imitation learning, is a powerful mechanism for reducing the complexity of search spaces for learning. When observing either good or bad examples, one can reduce the search for a possible solution,

Abstract—This article reports on the results of a user study investigating the satisfaction of naïve users conducting two learning by demonstration tasks with the HOAP-3 robot. The main goal of this study was to gain insights on how to ensure a successful as well as satisfactory experience for naïve users. Participants performed two tasks: They taught the robot to (1) push a box, and to (2) close a box. The user study was accompanied by three pre-structured questionnaires, addressing the users ’ satisfaction with HOAP-3, the user’s affect toward the robot following from the interaction, and the user’s attitude towards robots. Furthermore, a retrospective think aloud was conducted to gain a better understanding of what influences the users ’ satisfaction in learning by demonstration tasks. The results stress that learning by demonstration is a promising approach for naïve users to learn the interaction with a robot, as a high task completion and final satisfaction rate could be observed. Moreover, the short term interaction with HOAP-3 led to a positive affect higher than the normative average on half of the female users. I.

The paper describes and formalizes the concepts and assumptions involved in Learning from Demonstration (LFD), a common learning technique used in robotics. Inspired by the work on planning and actuation by LaValle [31], common LFD-related concepts like goal, generalization, and repetition are here defined, analyzed, and put into context. Robot behaviors are described in terms of trajectories through information spaces and learning is formulated as the mappings between some of these spaces. Finally, behavior primitives are introduced as one example of useful bias in the learning process, dividing the learning process into the three stages of behavior segmentation, behavior recognition, and behavior coordination.

I would like to thank many people who have supported me along the way and who have helped facilitate this work in different ways. First and foremost, I would like to thank my advisors. I would like to thank H. Levent Akın for his way of broad thinking, and for always encouraging me to dare to think different. He has played a key role in my transition from a fresh graduate into a scientist over the past years. I would like to thank Manuela Veloso for her guidance and passion. This thesis would not have been possible without Manuela being a constant source of inspiration. Her supernatural ability to say “No ” has helped enormously to keep me focused and on track through the inevitable research setbacks. My sincere thanks go to my thesis committee: Ethem Alpaydın, Ya˘gmur Denizhan, and Hatice Köse Ba˘gcı. I have learned almost all I know about machine learning and experiment design from Prof. Alpaydın. Our conversations with Prof. Denizhan had always been very inspiring and sparkling with her broad knowledge, and her unconventional way of tackling research problems. Starting from the beginning of my graduate life, Prof. Ba˘gcı had always been a good role model, and had influenced me in many ways along the prickly path of becoming a researcher. Parts of this thesis study were supported by The Scientific and Technological