Learning Robot Motion from Creative Human Demonstration

This thesis presents a learning from demonstration framework that enables a robot to learn and perform creative motions from human demonstrations in real-time. In order to satisfy all of the functional requirements for the framework, the developed technique is comprised of two modular components, which integrate together to provide the desired functionality. The first component, called Dancing from Demonstration (DfD), is a kinesthetic learning from demonstration technique. This technique is capable of playing back newly learned motions in real-time, as well as combining multiple learned motions together in a configurable way, either to reduce trajectory error or to generate entirely novel motions based on user specified parameters. DfD was utilized to enable a cooperative robot-human dance performance, and that performance has been evaluated to demonstrate that DfD achieved its design goals. The second component of this newly developed robot control framework, called Pose Energy Correspondence Mapping (PECM), is a passive-observation based learning from demonstration technique which is used to convert human pose trajectories into corresponding robot joint trajectories. This conversion process makes use of an energy based neural network model in order to attempt to achieve high quality results with a minimally sized training set. These two components have been combined together and the resulting framework has been evaluated by means of a comparative survey between human-generated robot motions, PECM-generated robot motions, and robot motions generated by a baseline neural network technique. These survey results are analyzed and discussed in order to identify the strengths and limitations of the newly developed framework

An enhanced teaching interface for a robot using DMP and GMR

This paper develops an enhanced teaching interface tested on both a Baxter robot and a KUKA iiwa robot. Movements are collected from a human demonstrator by using a Kinect v2 sensor, and then the data is sent to a remote PC for the teleoperation with Baxter. Meanwhile, data is saved locally for the playback process of the Baxter. The dynamic movement primitive (DMP) is used to model and generalize the movements. In order to learn from multiple demonstrations accurately, dynamic time warping (DTW), is used to pretreat the data recorded by the robot platform and Gaussian mixture model (GMM), aiming to generate multiple patterns after the teaching process, are employed for the calculation of the DMP. Then the Gaussian mixture regression (GMR) algorithm is applied to generate a synthesized trajectory with smaller position errors in 3D space. This proposed approach is tested by performing two tasks on a KUKA iiwa and a Baxter robot

Learning Algorithm Design for Human-Robot Skill Transfer

In this research, we develop an intelligent learning scheme for performing human-robot skills transfer. Techniques adopted in the scheme include the Dynamic Movement Prim- itive (DMP) method with Dynamic Time Warping (DTW), Gaussian Mixture Model (G- MM) with Gaussian Mixture Regression (GMR) and the Radical Basis Function Neural Networks (RBFNNs). A series of experiments are conducted on a Baxter robot, a NAO robot and a KUKA iiwa robot to verify the effectiveness of the proposed design.During the design of the intelligent learning scheme, an online tracking system is de- veloped to control the arm and head movement of the NAO robot using a Kinect sensor. The NAO robot is a humanoid robot with 5 degrees of freedom (DOF) for each arm. The joint motions of the operator’s head and arm are captured by a Kinect V2 sensor, and this information is then transferred into the workspace via the forward and inverse kinematics. In addition, to improve the tracking performance, a Kalman filter is further employed to fuse motion signals from the operator sensed by the Kinect V2 sensor and a pair of MYO armbands, so as to teleoperate the Baxter robot. In this regard, a new strategy is developed using the vector approach to accomplish a specific motion capture task. For instance, the arm motion of the operator is captured by a Kinect sensor and programmed through a processing software. Two MYO armbands with embedded inertial measurement units are worn by the operator to aid the robots in detecting and replicating the operator’s arm movements. For this purpose, the armbands help to recognize and calculate the precise velocity of motion of the operator’s arm. Additionally, a neural network based adaptive controller is designed and implemented on the Baxter robot to illustrate the validation forthe teleoperation of the Baxter robot.Subsequently, an enhanced teaching interface has been developed for the robot using DMP and GMR. Motion signals are collected from a human demonstrator via the Kinect v2 sensor, and the data is sent to a remote PC for teleoperating the Baxter robot. At this stage, the DMP is utilized to model and generalize the movements. In order to learn from multiple demonstrations, DTW is used for the preprocessing of the data recorded on the robot platform, and GMM is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. Next, we apply the GMR algorithm to generate a synthesized trajectory to minimize position errors in the three dimensional (3D) space. This approach has been tested by performing tasks on a KUKA iiwa and a Baxter robot, respectively.Finally, an optimized DMP is added to the teaching interface. A character recombination technology based on DMP segmentation that uses verbal command has also been developed and incorporated in a Baxter robot platform. To imitate the recorded motion signals produced by the demonstrator, the operator trains the Baxter robot by physically guiding it to complete the given task. This is repeated five times, and the generated training data set is utilized via the playback system. Subsequently, the DTW is employed to preprocess the experimental data. For modelling and overall movement control, DMP is chosen. The GMM is used to generate multiple patterns after implementing the teaching process. Next, we employ the GMR algorithm to reduce position errors in the 3D space after a synthesized trajectory has been generated. The Baxter robot, remotely controlled by the user datagram protocol (UDP) in a PC, records and reproduces every trajectory. Additionally, Dragon Natural Speaking software is adopted to transcribe the voice data. This proposed approach has been verified by enabling the Baxter robot to perform a writing task of drawing robot has been taught to write only one character

On the role of gestures in human-robot interaction

This thesis investigates the gestural interaction problem and in particular the usage of gestures for human-robot interaction. The lack of a clear definition of the problem statement and a common terminology resulted in a fragmented field of research where building upon prior work is rare. The scope of the research presented in this thesis, therefore, consists in laying the foundation to help the community to build a more homogeneous research field. The main contributions of this thesis are twofold: (i) a taxonomy to define gestures; and (ii) an ingegneristic definition of the gestural interaction problem. The contributions resulted is a schema to represent the existing literature in a more organic way, helping future researchers to identify existing technologies and applications, also thanks to an extensive literature review. Furthermore, the defined problem has been studied in two of its specialization: (i) direct control and (ii) teaching of a robotic manipulator, which leads to the development of technological solutions for gesture sensing, detection and classification, which can possibly be applied to other contexts

Smart Navigation in Surgical Robotics

La cirugía mínimamente invasiva, y concretamente la cirugía laparoscópica, ha supuesto un gran cambio en la forma de realizar intervenciones quirúrgicas en el abdomen. Actualmente, la cirugía laparoscópica ha evolucionado hacia otras técnicas aún menos invasivas, como es la cirugía de un solo puerto, en inglés Single Port Access Surgery. Esta técnica consiste en realizar una única incisión, por la que son introducidos los instrumentos y la cámara laparoscópica a través de un único trocar multipuerto. La principal ventaja de esta técnica es una reducción de la estancia hospitalaria por parte del paciente, y los resultados estéticos, ya que el trocar se suele introducir por el ombligo, quedando la cicatriz oculta en él. Sin embargo, el hecho de que los instrumentos estén introducidos a través del mismo trocar hace la intervención más complicada para el cirujano, que necesita unas habilidades específicas para este tipo de intervenciones. Esta tesis trata el problema de la navegación de instrumentos quirúrgicos mediante plataformas robóticas teleoperadas en cirugía de un solo puerto. En concreto, se propone un método de navegación que dispone de un centro de rotación remoto virtual, el cuál coincide con el punto de inserción de los instrumentos (punto de fulcro). Para estimar este punto se han empleado las fuerzas ejercidas por el abdomen en los instrumentos quirúrgicos, las cuales han sido medidas por sensores de esfuerzos colocados en la base de los instrumentos. Debido a que estos instrumentos también interaccionan con tejido blando dentro del abdomen, lo cual distorsionaría la estimación del punto de inserción, es necesario un método que permita detectar esta circunstancia. Para solucionar esto, se ha empleado un detector de interacción con tejido basado en modelos ocultos de Markov el cuál se ha entrenado para detectar cuatro gestos genéricos. Por otro lado, en esta tesis se plantea el uso de guiado háptico para mejorar la experiencia del cirujano cuando utiliza plataformas robóticas teleoperadas. En concreto, se propone la técnica de aprendizaje por demostración (Learning from Demonstration) para generar fuerzas que puedan guiar al cirujano durante la resolución de tareas específicas. El método de navegación propuesto se ha implantado en la plataforma quirúrgica CISOBOT, desarrollada por la Universidad de Málaga. Los resultados experimentales obtenidos validan tanto el método de navegación propuesto, como el detector de interacción con tejido blando. Por otro lado, se ha realizado un estudio preliminar del sistema de guiado háptico. En concreto, se ha empleado una tarea genérica, la inserción de una clavija, para realizar los experimentos necesarios que permitan demostrar que el método propuesto es válido para resolver esta tarea y otras similares

ハンドとひもの関係に基づくひも結び作業の実行

電気通信大学201