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

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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Ensuring kinetostatic consistency in observation of human manipulation

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ROBOTICS AND AUTONOMOUS SYSTEMS 2013年第5期61卷 545-553页

作者： Falco, Pietro Natale, Ciro Dillmann, Ruediger Univ Naples 2 Dipartimento Ingn Ind & Informaz I-81031 Aversa Italy Karlsruhe Inst Technol Inst Anthropomat D-76131 Karlsruhe Germany

The aim of this paper is to present a method to guarantee the kinetostatic consistency in observation of human manipulation, i.e. the consistency between the observed hand posture and the tactile information on the contact between the fingertips and the objects. The core idea of the proposed algorithm is to compare the fingertip contact information, obtained by tactile sensors, with the contact information computed in a virtual environment, that reproduces the real environment where the observation is carried out. In case the estimation of the joint angles and the relative pose between the hand and the object are not consistent, a correction of the hand posture is computed. For some tasks, collisions might occur between parts of the hand (e.g. palm) and the grasped object. To handle this problem, the corrected hand posture is computed by adopting a closed loop inverse kinematic (CLIK) approach that exploits the redundant Degrees of Freedom (DoFs) of the hand. The algorithm has been designed to work on-line. This feature is particularly important for programming by demonstration (PbD) applications, since it allows the trainer to actively adapt the demonstration to measurement noise and model errors. The effectiveness of the proposed method has been tested in five different tasks: grasping a cup, unscrewing a bottle, grasping a plate, grasping a ketchup bottle, and grasping a measuring cup. (C) 2012 Elsevier B.V. All rights reserved.

关键词： programming by demonstration Human manipulation On-line multimodal observation Inverse Kinematics

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Generalization of orientation trajectories and force-torque profiles for robotic assembly

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ROBOTICS AND AUTONOMOUS SYSTEMS 2017年 98卷 333-346页

作者： Kramberger, Aljaz Gams, Andrej Nemec, Bojan Chrysostomou, Dimitrios Madsen, Ole Ude, Ales Jozef Stefan Inst Dept Automat Biocybernet & Robot Humanoid & Cognit Robot Lab Jamova Cesta 39 Ljubljana 1000 Slovenia Aalborg Univ Dept Mech & Mfg Engn Robot & Automat Grp Fibigerstraede 16 DK-9220 Aalborg Denmark

A typical robot assembly operation involves contacts with the parts of the product to be assembled and consequently requires the knowledge of not only position and orientation trajectories but also the accompanying force-torque profiles for successful performance. To learn the execution of assembly operations even when the geometry of the product varies across task executions, the robot needs to be able to adapt its motion based on a parametric description of the current task condition, which is usually provided by geometrical properties of the parts involved in the assembly. In our previous work we showed how positional control policies can be generalized to different task conditions. In this paper we propose a complete methodology to generalize also the orientational trajectories and the accompanying force-torque profiles to compute the necessary control policy for a given condition of the assembly task. Our method is based on statistical generalization of successfully recorded executions at different task conditions, which are acquired by kinesthetic guiding. The parameters that describe the varying task conditions define queries into the recorded training data. To improve the execution of the skill after generalization, we combine the proposed approach with an adaptation method, thus enabling the refinement of the generalized assembly operation. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Trajectory generalization Trajectory adaptation programming by demonstration

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Humanoid Tactile Gesture Production using a Hierarchical SOM-based Encoding

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IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT 2014年第2期6卷 153-167页

作者： Pierris, Georgios Dahl, Torbjorn S. Univ Wales Cognit Robot Res Ctr Newport NP18 3QT Gwent Wales Univ Plymouth Cognit Inst Plymouth 03264 Devon England

The existence of cortical hierarchies has long since been established and the advantages of hierarchical encoding of sensor-motor data for control, have long been recognized. Less well understood are the developmental processes whereby such hierarchies are constructed and subsequently used. This paper presents a new algorithm for encoding sequential sensor and actuator data in a dynamic, hierarchical neural network that can grow to accommodate the length of the observed interactions. The algorithm uses a developmental robotics methodology as it extends the Constructivist Learning Architecture, a computational theory of infant cognitive development. This paper presents experimental data demonstrating how the extended algorithm goes beyond the original theory by supporting goal oriented control. The domain studied is the encoding and reproduction of tactile gestures in humanoid robots. In particular, we present results from using a programming by demonstration approach to encode a stroke gesture. Our results demonstrate how the novel encoding enables a Nao humanoid robot with a touch sensitive fingertip to successfully encode and reproduce a stroke gesture in the presence of perturbations from internal and external forces.

关键词： Artificial neural networks programming by demonstration tactile gestures

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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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Training agents to recognize text by example

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AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS 2001年第1-2期4卷 79-92页

作者： Lieberman, H Nardi, BA Wright, DJ MIT Media Lab Cambridge MA 02139 USA AT&T Labs W Menlo Park CA USA Apple Comp Inc Cupertino CA 95014 USA

An important function of an agent is to be "on the lookout" for bits of information that are interesting to its user, even if these items appear in the midst of a larger body of unstructured information. But how to tell these agents which patterns are meaningful and what to do with the result? Especially when agents are used to recognize text, they are usually driven by parsers which require input in the form of textual grammar rules. Editing grammars is difficult and error-prone for end users. Grammar ["Grammars by Example"] is the first direct manipulation interface designed to allow nonexpert users to define grammars interactively. The user presents concrete examples of text that he or she would like the agent to recognize. Rules are constructed by an iterative process, where Grammex heuristically parses the example, displays a set of hypotheses, and the user critiques the system's suggestions. Actions to take upon recognition are also demonstrated by example.

关键词： agents parsers grammars programming by example programming by demonstration

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