Multiform Adaptive Robot Skill Learning from Humans

Object manipulation is a basic element in everyday human lives. Robotic manipulation has progressed from maneuvering single-rigid-body objects with firm grasping to maneuvering soft objects and handling contact-rich actions. Meanwhile, technologies such as robot learning from demonstration have enabled humans to intuitively train robots. This paper discusses a new level of robotic learning-based manipulation. In contrast to the single form of learning from demonstration, we propose a multiform learning approach that integrates additional forms of skill acquisition, including adaptive learning from definition and evaluation. Moreover, going beyond state-of-the-art technologies of handling purely rigid or soft objects in a pseudo-static manner, our work allows robots to learn to handle partly rigid partly soft objects with time-critical skills and sophisticated contact control. Such capability of robotic manipulation offers a variety of new possibilities in human-robot interaction.Comment: Accepted to 2017 Dynamic Systems and Control Conference (DSCC), Tysons Corner, VA, October 11-1

Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies

Robots are increasingly entering uncertain and unstructured environments. Within these, robots are bound to face unexpected external disturbances like accidental human or tool collisions. Robots must develop the capacity to respond to unexpected events. That is not only identifying the sudden anomaly, but also deciding how to handle it. In this work, we contribute a recovery policy that allows a robot to recovery from various anomalous scenarios across different tasks and conditions in a consistent and robust fashion. The system organizes tasks as a sequence of nodes composed of internal modules such as motion generation and introspection. When an introspection module flags an anomaly, the recovery strategy is triggered and reverts the task execution by selecting a target node as a function of a state dependency chart. The new skill allows the robot to overcome the effects of the external disturbance and conclude the task. Our system recovers from accidental human and tool collisions in a number of tasks. Of particular importance is the fact that we test the robustness of the recovery system by triggering anomalies at each node in the task graph showing robust recovery everywhere in the task. We also trigger multiple and repeated anomalies at each of the nodes of the task showing that the recovery system can consistently recover anywhere in the presence of strong and pervasive anomalous conditions. Robust recovery systems will be key enablers for long-term autonomy in robot systems. Supplemental info including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl

Compensation for undefined behaviors during robot task execution by switching controllers depending on embedded dynamics in RNN

Robotic applications require both correct task performance and compensation for undefined behaviors. Although deep learning is a promising approach to perform complex tasks, the response to undefined behaviors that are not reflected in the training dataset remains challenging. In a human-robot collaborative task, the robot may adopt an unexpected posture due to collisions and other unexpected events. Therefore, robots should be able to recover from disturbances for completing the execution of the intended task. We propose a compensation method for undefined behaviors by switching between two controllers. Specifically, the proposed method switches between learning-based and model-based controllers depending on the internal representation of a recurrent neural network that learns task dynamics. We applied the proposed method to a pick-and-place task and evaluated the compensation for undefined behaviors. Experimental results from simulations and on a real robot demonstrate the effectiveness and high performance of the proposed method.Comment: To appear in IEEE Robotics and Automation Letters (RA-L) and IEEE International Conference on Robotics and Automation (ICRA 2021

Programming by demonstration of the sequence of tightening a nut allowing variations in tool position

Se presenta una técnica que permite la programación por demostración de un robot para que ejecute una tarea secuencial o compleja. Se utiliza una combinación de redes de Petri y modelos de mezcla de gaussianas parametrizado en la tarea; con la primera se coordina la secuencia de la tarea, en tanto que la segunda permite variaciones en la posición y orientación de los objetos de la misma. Una técnica de segmentación de tareas, descompone la demostración en subtareas. Con la secuencia de las subtareas, se obtiene una lista de acciones (plan) y con este se genera de manera automática una red de Petri. A la técnica también se le suministran las plantillas modelo de cada subtarea y los modelos de mezcla de gaussianas parametrizados en la tarea de las trayectorias de la subtarea que se quiere que admita variaciones. Una función compara las trayectorias de cada plantilla con las trayectorias repuesta del modelo, y la de mayor similitud indica que en vez de la plantilla, se debe emplear el modelo de mezcla parametrizado. Mediante el uso de un robot de fabricación propia, el cual ejecuta la tarea de tomar, transportar una llave y apretar una tuerca, se ilustra el desempeño de la técnica a través de gráficas.A technique of programming by demonstration of a robot is proposed. Such a technique allows that a robot execute sequential or complex tasks. It uses a combination of Petri nets and task parameterized Gaussian mixture models. The first one handles the task sequence, while the second one allows variations in the position and orientation of objects involved in the task. Using a segmentation task technique, the demonstration is chunked in subtasks. With the subtasks sequence, an action list or plan is obtained and with this, a Petri net is automatically generate. Models of the templates of each subtasks and task parameterized Gaussian mixture models of the subtask that we want to allow variations are also provide to the technique. A function compare one each of the template trajectory with the task parameterized model response trajectory and the most similar indicate that instead of the template, the task parameterized model is use. Through the use of a homemade robot, which executes the task of tightening a nut, the performance of the technique is illustrated by using figures

Execution fault recovery in robot programming by demonstration using multiple models

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Deformable object (e.g., clothes) manipulation by a robot in interaction with a human being presents several interesting challenges. Due to texture and deformability, the object can get hooked in the human limbs. Moreover, the human can change their limbs position and curvature, which require changes in the paths to be followed by the robot. To help solve these problems, in this paper we propose a technique of learning by demonstration able to adapt to changes in position and curvature of the object (human limb) and recover from execution faults (hooks). The technique is tested using simulations, but with data obtained from a real robotPeer ReviewedPostprint (author's final draft

Learning and Execution of Object Manipulation Tasks on Humanoid Robots

Equipping robots with complex capabilities still requires a great amount of effort. In this work, a novel approach is proposed to understand, to represent and to execute object manipulation tasks learned from observation by combining methods of data analysis, graphical modeling and artificial intelligence. Employing this approach enables robots to reason about how to solve tasks in dynamic environments and to adapt to unseen situations

An agile and adaptive holonic architecture for manufacturing control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2004. Faculdade de Engenharia. Universidade do Port

Fujaba days 2009 : proceedings of the 7th international Fujaba days, Eindhoven University of Technology, the Netherlands, November 16-17, 2009

Fujaba is an Open Source UML CASE tool project started at the software engineering group of Paderborn University in 1997. In 2002 Fujaba has been redesigned and became the Fujaba Tool Suite with a plug-in architecture allowing developers to add functionality easily while retaining full control over their contributions. Multiple Application Domains Fujaba followed the model-driven development philosophy right from its beginning in 1997. At the early days, Fujaba had a special focus on code generation from UML diagrams resulting in a visual programming language with a special emphasis on object structure manipulating rules. Today, at least six rather independent tool versions are under development in Paderborn, Kassel, and Darmstadt for supporting (1) reengineering, (2) embedded real-time systems, (3) education, (4) specification of distributed control systems, (5) integration with the ECLIPSE platform, and (6) MOF-based integration of system (re-) engineering tools. International Community According to our knowledge, quite a number of research groups have also chosen Fujaba as a platform for UML and MDA related research activities. In addition, quite a number of Fujaba users send requests for more functionality and extensions. Therefore, the 7th International Fujaba Days aimed at bringing together Fujaba developers and Fujaba users from all over the world to present their ideas and projects and to discuss them with each other and with the Fujaba core development team