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Correspondence mapping induced state and action metrics for robotic imitation

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2007年第2期37卷 299-307页

作者： Alissandrakis, Aris Nehaniv, Chrystopher L. Dautenhahn, Kerstin Univ Hertfordshire Sch Comp Sci Adapt Syst Res Grp Hatfield AL10 9AB Herts England

This paper addresses the problem of body mapping in robotic imitation where the demonstrator and imitator may not share the same embodiment [degrees of freedom (DOFs), body morphology, constraints, affordances, and so on]. Body mappings are formalized using a unified (linear) approach via correspondence matrices, which allow one to capture partial, mirror symmetric, one-to-one, one-to-many, many-to-one, and many-to-many associations between various DOFs across dissimilar embodiments. We show how metrics for matching state and action aspects of behavior can be mathematically determined by such correspondence mappings, which may serve to guide a robotic imitator. The approach is illustrated and validated in a number of simulated 3-D robotic examples, using agents described by simple kinematic models and different types of correspondence mappings.

关键词： correspondence problem imitation and social learning programming by demonstration state and action metrics

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Learning robot motions with stable dynamical systems under diffeomorphic transformations

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ROBOTICS AND AUTONOMOUS SYSTEMS 2015年 70卷 1-15页

作者： Neumann, Klaus Steil, Jochen J. Univ Bielefeld Res Inst Cognit & Robot CoR Lab Bielefeld Germany

Accuracy and stability have in recent studies been emphasized as the two major ingredients to learn robot motions from demonstrations with dynamical systems. Several approaches yield stable dynamical systems but are also limited to specific dynamics that can potentially result in a poor reproduction performance. The current work addresses this accuracy-stability dilemma through a new diffeomorphic transformation approach that serves as a framework generalizing the class of demonstrations that are learnable by means of provably stable dynamical systems. We apply the proposed framework to extend the application domain of the stable estimator of dynamical systems (SEDS) by generalizing the class of learnable demonstrations by means of diffeomorphic transformations tau. The resulting approach is named tau-SEDS and analyzed with rigorous theoretical investigations and robot experiments. (C) 2015 Elsevier B.V. All rights reserved.

关键词： SEDS Imitation learning programming by demonstration Robotics Dynamical system Stability

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A robot learning from demonstration framework to perform force-based manipulation tasks

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INTELLIGENT SERVICE ROBOTICS 2013年第1期6卷 33-51页

作者： Rozo, Leonel Jimenez, Pablo Torras, Carme UPC CSIC Percept & Manipulat Grp Inst Robot & Informat Ind Barcelona 08028 Spain

This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are many fold. First, we deal with the problem of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human-robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known what to imitate? problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher's demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher's one.

关键词： programming by demonstration Imitation learning Haptic perception Mutual information HMM GMR Robotic manipulation

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Incremental learning of tasks from user demonstrations, past experiences, and vocal comments

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2007年第2期37卷 322-332页

作者： Pardowitz, Michael Knoop, Steffen Dillmann, Ruediger Zoellner, Raoul D. Univ Karlsruhe TH Inst Comp Sci & Engn D-76138 Karlsruhe Germany Siemens Corp Technol Informat & Commun D-81739 Munich Germany

Since many years the robotics community is envisioning robot assistants sharing the same environment with humans. It became obvious that they have to interact with humans and should adapt to individual user needs. Especially the high variety of tasks robot assistants will be facing requires a highly adaptive and user-friendly programming interface. One possible solution to this programming problem is the learning-by-demonstration paradigm, where the robot is supposed to observe the execution of a task, acquire task knowledge, and reproduce it. In this paper, a system to record, interpret, and reason over demonstrations of household tasks is presented. The focus is on the model-based representation of manipulation tasks, which serves as a basis for incremental reasoning over the acquired task knowledge. The aim of the reasoning is. to condense and interconnect the data, resulting in more general task knowledge. A measure for the assessment of information content of task features is introduced. This measure for the relevance of certain features relies both on general background knowledge as well as task-specific knowledge gathered from the user demonstrations. Beside the autonomous information estimation of features, speech comments during the execution, pointing out the relevance of features are considered as well. The results of the incremental growth of the task knowledge when more task demonstrations become available and their fusion with relevance information gained from speech comments is demonstrated within the task of laying a table.

关键词： human-robot interaction programming by demonstration task learning

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A human inspired handover policy using Gaussian Mixture Models and haptic cues

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AUTONOMOUS ROBOTS 2019年第6期43卷 1327-1342页

作者： Sidiropoulos, Antonis Psomopoulou, Efi Doulgeri, Zoe Ctr Res & Technol Hellas CERTH Thessaloniki 57001 Greece Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece

A handover strategy is proposed that aims at natural and fluent robot to human object handovers. For the approaching phase, a globally asymptotically stable dynamical system (DS) is utilized, trained from human demonstrations and exploiting the existence of mirroring in the human wrist motion. The DS operates in the robot task space thus achieving independence with respect to the robot platform, encapsulating the position and orientation of the human wrist within a single DS. It is proven that the motion generated by such a DS, having as target the current wrist pose of the receiver's hand, is bounded and converges to the previously unknown handover location. Haptic cues based on load estimates at the robot giver ensure full object load transfer before grip release. The proposed strategy is validated with simulations and experiments in real settings.

关键词： programming by demonstration Gaussian Mixture Model Physical human-robot interaction Haptic communication

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Incremental Learning of Skills in a Task-Parameterized Gaussian Mixture Model

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2016年第1期82卷 81-99页

作者： Hoyos, Jose Prieto, Flavio Alenya, Guillem Torras, Carme Univ Quindio Av Bolivar Cl 12N Armenia Colombia Univ Nacl Colombia Carrera 30 45-03 Bogota Colombia CSIC UPC Parc Tecnol Barcelona Inst Robot & Informat Ind C Llorens i Artigas 4-6 Barcelona Spain

programming by demonstration techniques facilitate the programming of robots. Some of them allow the generalization of tasks through parameters, although they require new training when trajectories different from the ones used to estimate the model need to be added. One of the ways to re-train a robot is by incremental learning, which supplies additional information of the task and does not require teaching the whole task again. The present study proposes three techniques to add trajectories to a previously estimated task-parameterized Gaussian mixture model. The first technique estimates a new model by accumulating the new trajectory and the set of trajectories generated using the previous model. The second technique permits adding to the parameters of the existent model those obtained for the new trajectories. The third one updates the model parameters by running a modified version of the Expectation-Maximization algorithm, with the information of the new trajectories. The techniques were evaluated in a simulated task and a real one, and they showed better performance than that of the existent model.

关键词： programming by demonstration Robot learning Incremental learning

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Teaching the Teacher: Tutoring SimStudent Leads to More Effective Cognitive Tutor Authoring

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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION 2015年第1期25卷 1-34页

作者： Matsuda, Noboru Cohen, William W. Koedinger, Kenneth R. Carnegie Mellon Univ Human Comp Interact Inst 5000 Forbes Ave Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA

SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitive tutor by tutoring SimStudent on how to solve problems. There are two different ways to author an expert model with SimStudent. In the context of Authoring by Tutoring, the author interactively tutors SimStudent by posing problems to SimStudent, providing feedback on the steps performed by SimStudent, and also demonstrating steps as a response to SimStudent's hint requests when SimStudent cannot perform steps correctly. In the context of Authoring by demonstration, the author demonstrates solution steps, and SimStudent attempts to induce underlying domain principles by generalizing those worked-out examples. We conducted evaluation studies to investigate which authoring strategy better facilitates authoring and found two key results. First, the expert model generated with Authoring by Tutoring is better and has higher accuracy while maintaining the same level of completeness than the one generated with Authoring by demonstration. The reason for this better accuracy is that the expert model generated by tutoring benefits from negative feedback provided for SimStudent's incorrect production applications. Second, authoring by Tutoring requires less time than Authoring by demonstration. This enhanced authoring efficiency is partially because (a) when Authoring by demonstration, the author needs to test the quality of the expert model, whereas the formative assessment of the expert model is done naturally by observing SimStudent's performance when Authoring by Tutoring, and (b) the number of steps that need to be demonstrated during tutoring decreases as learning progresses.

关键词： Intelligent authoring system Machine learning programming by demonstration Inductive logic programming Cognitive tutor authoring tools SimStudent

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Dynamic Grasp Recognition Using Time Clustering, Gaussian Mixture Models and Hidden Markov Models

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ADVANCED ROBOTICS 2009年第10期23卷 1359-1371页

作者： Ju, Zhaojie Liu, Honghai Zhu, Xiangyang Xiong, Youlun Univ Portsmouth Inst Ind Res Portsmouth PO1 3QL Hants England Shanghai Jiao Tong Univ Inst Robot Shanghai 200204 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Huazong 430074 Peoples R China

The human hand has the capability of fulfilling various everyday-life tasks using the combination of biological mechanisms, sensors and controls. Autonomously controlling multifingered robots is a challenge, which holds the key to related multidisciplinary research and a wide spectrum of applications in intelligent robotics. We demonstrate the state of the art in recognizing continuous grasping gestures of human hands in this paper. We propose a novel time clustering method (TC) and modified Gaussian mixture models (GMMs) and compare them with hidden Markov models (HMMs). The TC outperforms the GMM and HMM methods in terms of recognition rate and potentially in computational cost. Future work is focused on real-time recognition and grasp qualitative description. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2009

关键词： Grasp recognition time clustering Gaussian mixture models hidden Markov models programming by demonstration

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End-User Development of Mashups with NaturalMash

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JOURNAL OF VISUAL LANGUAGES AND COMPUTING 2014年第4期25卷 414-432页

作者： Aghaee, Saeed Pautasso, Cesare Univ Lugano USI Fac Informat Lugano Switzerland

Context: The emergence of the long-tail in the market of software applications is shifting the role of end-users from mere consumers to becoming developers of applications addressing their unique, personal, and transient needs. On the Web, a popular form of such applications is called mashup, built out of the lightweight composition of Web APIs (reusable software components delivered as a service through the Web). To enable end-users to build mashups, there is a key problem that must be overcome: End-users lack programming knowledge as well as the interest to learn how to master the complex set of Web technologies required to develop mashups. End-User Development (EUD) is an emerging research field dealing with this type of problems. Its main goal is to design tools and techniques facilitating the development of software applications by non-programmers. Objective: The paper describes the design and evaluation of NaturalMash, an innovative EUD tool for mashups (a mashup tool). NaturalMash aims at enabling non-professional users without any knowledge of programming languages and skills to create feature-rich, interactive, and useful mashups. Methods: The design of NaturalMash adopts a formative evaluation approach, and has completed three design and evaluation iterations. The formative evaluations utilize usability testing, think aloud protocol, questionnaires, observation, and unstructured interviews. Additionally, we compare the expressive power of naturalmash with the state-of-the-art mashup tools. Results: The results from the formative evaluations helped us identify important usability problems. From an assessment point of view, the results were promising and sggested that the proposed tool has a short and gentle learning curve in a way that even non-programmers are able to rapidly build useful mashups. Also, the comparative evaluation results showed that NaturalMash offers a competitive level of expressive power compared with existing mashup tools targeting non-

关键词： Mashups End-User Development Mashup tools WYSIWYG Natural language programming programming by demonstration

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An Electroencephalography Study on Cognitive Load in Visual and Textual programming 24

An Electroencephalography Study on Cognitive Load in Visual ...

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20th Annual Conference on International Computing Education Research (ICER)

作者： Thorgeirsson, Sverrir Zhang, Chengyu Weidmann, Theo B. Weidmann, Karl-Heinz Su, Zhendong Swiss Fed Inst Technol Zurich Switzerland Univ Appl Sci Vorarlberg Dornbirn Austria

ISBN: (纸本)9798400704758

This paper presents a comparative study of Algot, a visual programming language designed to bridge the syntax-semantics gap via liveness and programming by demonstration, and the textual programming language Python. We conducted an experimental, within-subjects study with 24 undergraduate computer science students who performed recursion-based tasks in each language while their cognitive load was measured using an electroencephalogram and a validated survey instrument. The students received a brief introduction to Algot, but were all familiar with Python. The students performed significantly better when programming in Algot, but the cognitive load levels were similar according to both instruments. Our results provide evidence that within the domain that was tested, Algot can be quickly learned, and that students do not find it more cognitively demanding than working in a familiar language.

关键词： electroencephalography undergraduate education CS1 recursion cognitive load visual programming programming by demonstration direct manipulation live programming

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