Motion Imitation Based on Sparsely Sampled Correspondence

Existing techniques for motion imitation often suffer a certain level of latency due to their computational overhead or a large set of correspondence samples to search. To achieve real-time imitation with small latency, we present a framework in this paper to reconstruct motion on humanoids based on sparsely sampled correspondence. The imitation problem is formulated as finding the projection of a point from the configuration space of a human's poses into the configuration space of a humanoid. An optimal projection is defined as the one that minimizes a back-projected deviation among a group of candidates, which can be determined in a very efficient way. Benefited from this formulation, effective projections can be obtained by using sparse correspondence. Methods for generating these sparse correspondence samples have also been introduced. Our method is evaluated by applying the human's motion captured by a RGB-D sensor to a humanoid in real-time. Continuous motion can be realized and used in the example application of tele-operation.Comment: 8 pages, 8 figures, technical repor

Inserção de um robô humanoide no ensino de objetos geométricos 2D sobrepostos

Robotics, introduced into educational environments, can be an interesting alternative to explore theoretical concepts covered in class, faciliting learning and engaging the student’s interest.In this paper, a computational system capable of detect objects was incorporated into the robot NAO, so that can interact with students, recognizing geometric shapes with overlap. The system consists of two models of neural networks and was evaluated through a sequence of didatic activities presented to students of the 5th year, aiming to encourage them. The robot operates autonomously, recognizing and counting the diferente objects in the image. The results show that the children felt very motivated and engaged to fulfill the tasks.A robótica, inserida em ambientes educacionais, é uma alternativa interessante para explorar conceitos teóricos abordados em sala de aula, facilitando o aprendizado e cativando o interesse dos alunos. Neste artigo, um sistema computacional capaz de detectar objetos foi incorporado ao robô NAO para que o mesmo possa interagir com alunos, reconhecendo figuras geométricas com sobreposição. O sistema é constituído por dois modelos de Redes Neurais e foi avaliado por meio de uma sequência de atividades didáticas apresentadas a alunos do 5o ano, visando estimulá-los. O robô atua autônomamente, reconhecendo e contando os diferentes objetos na imagem. Os resultados apresentados mostram que as crianças se sentiram muito motivadas para cumprir as tarefas

Gesture imitation and recognition using Kinect sensor and extreme learning machines

This study presents a framework that recognizes and imitates human upper-body motions in real time. The framework consists of two parts. In the first part, a transformation algorithm is applied to 3D human motion data captured by a Kinect. The data are then converted into the robot’s joint angles by the algorithm. The human upper-body motions are successfully imitated by the NAO humanoid robot in real time. In the second part, the human action recognition algorithm is implemented for upper-body gestures. A human action dataset is also created for the upper-body movements. Each action is performed 10 times by twenty-four users. The collected joint angles are divided into six action classes. Extreme Learning Machines (ELMs) are used to classify the human actions. Additionally, the Feed-Forward Neural Networks (FNNs) and K-Nearest Neighbor (K-NN) classifiers are used for comparison. According to the comparative results, ELMs produce a good human action recognition performance

Incorporating a humanoid robot to motivate the geometric figures learning

Technology has been introduced into educational environments to facilitate learning and engage the students interest. Robotics can be an interesting alternative to explore theoretical concepts covered in class. In this paper, a computational system capable of detecting objects was incorporated into the robot NAO, so it can Interact with students, recognizing geometric shapes with overlap. The system consists of two models of neural networks and was evaluated through a sequence of didatic activities presented to students of the 5th year, aiming to encourage them to perform the tasks. The robot operates autonomously, recognizing and counting the diferente objects in the image. The results show that the children felt very motivated and engaged to fulfill the tasks.São Paulo State Research Foundation (FAPESP)Brazilian National Research Council (CNPq

Exploring Natural User Abstractions For Shared Perceptual Manipulator Task Modeling & Recovery

State-of-the-art domestic robot assistants are essentially autonomous mobile manipulators capable of exerting human-scale precision grasps. To maximize utility and economy, non-technical end-users would need to be nearly as efficient as trained roboticists in control and collaboration of manipulation task behaviors. However, it remains a significant challenge given that many WIMP-style tools require superficial proficiency in robotics, 3D graphics, and computer science for rapid task modeling and recovery. But research on robot-centric collaboration has garnered momentum in recent years; robots are now planning in partially observable environments that maintain geometries and semantic maps, presenting opportunities for non-experts to cooperatively control task behavior with autonomous-planning agents exploiting the knowledge. However, as autonomous systems are not immune to errors under perceptual difficulty, a human-in-the-loop is needed to bias autonomous-planning towards recovery conditions that resume the task and avoid similar errors. In this work, we explore interactive techniques allowing non-technical users to model task behaviors and perceive cooperatively with a service robot under robot-centric collaboration. We evaluate stylus and touch modalities that users can intuitively and effectively convey natural abstractions of high-level tasks, semantic revisions, and geometries about the world. Experiments are conducted with \u27pick-and-place\u27 tasks in an ideal \u27Blocks World\u27 environment using a Kinova JACO six degree-of-freedom manipulator. Possibilities for the architecture and interface are demonstrated with the following features; (1) Semantic \u27Object\u27 and \u27Location\u27 grounding that describe function and ambiguous geometries (2) Task specification with an unordered list of goal predicates, and (3) Guiding task recovery with implied scene geometries and trajectory via symmetry cues and configuration space abstraction. Empirical results from four user studies show our interface was much preferred than the control condition, demonstrating high learnability and ease-of-use that enable our non-technical participants to model complex tasks, provide effective recovery assistance, and teleoperative control

Intelligent Management of Hierarchical Behaviors Using a NAO Robot as a Vocational Tutor

In order to create an intelligent system which can hold an interview using the NAO robot as an interviewer playing the role of a vocational tutor were classified and categorized twenty behaviors within five personality profiles. Five basic emotions are considered: Anger, boredom, interest, surprise and joy. Selected behaviors are grouped according to these five different emotions. Common behaviors (e.g., movements or body postures) used by the robot (who assumes the role of vocational tutor) during vocational guidance sessions will be based on a theory of personality traits called the "Five Factor Model". In this context, a predefined set of questions will be asked by the robot according to a theoretical model called "Orientation Model" about the person's vocational preferences. Therefore, NAO can react as conveniently as possible during the interview according to the score of the answer given by the person to the question posed and its personality type. Additionally, based on the answers to these questions, it is established a vocational profile and the robot can to emit a recommendation about person vocation. The results obtained show how the intelligent selection of behaviors can be successfully achieved through the proposed approach, making the interaction between a human and a robot friendlier