Robot Composite Learning and the Nunchaku Flipping Challenge

Advanced motor skills are essential for robots to physically coexist with humans. Much research on robot dynamics and control has achieved success on hyper robot motor capabilities, but mostly through heavily case-specific engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous manner, robot learning from human demonstration (LfD) has achieved great progress, but still has limitations handling dynamic skills and compound actions. In this paper, we present a composite learning scheme which goes beyond LfD and integrates robot learning from human definition, demonstration, and evaluation. The method tackles advanced motor skills that require dynamic time-critical maneuver, complex contact control, and handling partly soft partly rigid objects. We also introduce the "nunchaku flipping challenge", an extreme test that puts hard requirements to all these three aspects. Continued from our previous presentations, this paper introduces the latest update of the composite learning scheme and the physical success of the nunchaku flipping challenge

Vision-Based Control of the Robotenis System

In this paper a visual servoing architecture based on a parallel robot for the tracking of faster moving objects with unknown trajectories is proposed. The control strategy is based on the prediction of the future position and velocity of the moving object. The synthesis of the predictive control law is based on the compensation of the delay introduced by the vision system. Demonstrating by experiments, the high-speed parallel robot system has good performance in the implementation of visual control strategies with high temporary requirement

Visual Servoing for the Robotenis System: a Strategy for a 3 DOF Parallel Robot to Hit a Ping-Pong Ball

This article describes a new visual servo control and strategies that are used to carry out dynamic tasks by the Robotenis platform. This platform is basically a parallel robot that is equipped with an acquisition and processing system of visual information, its main feature is that it has a completely open architecture control, and planned in order to design, implement, test and compare control strategies and algorithms (visual and actuated joint controllers). Following sections describe a new visual control strategy specially designed to track and intercept objects in 3D space. The results are compared with a controller shown in previous woks, where the end effector of the robot keeps a constant distance from the tracked object. In this work, the controller is specially designed in order to allow changes in the tracking reference. Changes in the tracking reference can be used to grip an object that is under movement, or as in this case, hitting a hanging Ping-Pong ball. Lyapunov stability is taken into account in the controller design

Control Visual Dinámico en una PKM para el Seguimiento y Golpeo de una Pelota de Ping-Pong

En el presente trabajo se describe un controlador innovador que es utilizado en la realización de tareas dinámicas con aplicación en la plataforma Robotenis. Esta plataforma está compuesta principalmente por un robot paralelo dotado de un sistema de adquisición y procesamiento visual. La plataforma Robotenis tiene una arquitectura de control abierta con el objetivo de diseñar, implementar y comparar estrategias de control visual y articular. En las siguientes secciones se describe una nueva estrategia de control visual, especialmente diseñada para el seguimiento de objetos dinámicos en el espacio tridimensional. En contraste con estrategias estudiadas en trabajos previos, donde la referencia de control es constante, en este trabajo se contemplan referencias dinámicas de seguimiento. Mediante este controlador, los cambios de referencia pueden ser utilizados para sujetar objetos bajo movimiento o como en nuestro caso para golpear una pelota de ping-pong. En el diseño del controlador se realiza un análisis de estabilidad por Lyapunov

Dynamic Visual Servoing for Ping-Pong Game of a 3DOF PKM

This article describes the new visual servo control and strategies that are utilized to carry out dynamic tasks by the system Robotenis. This platform is a parallel robot that is equipped with an acquisition and processing system of visual information. Its main feature is that it has a completely open architecture control, planned in order to design, implement, test and compare control strategies and algorithms (visual and actuated joint controllers). Following sections describe a new visual control strategy specially designed to track dynamic objects in 3D space. Contrasting the strategies shown in previous works, where the end effect or of the robot keeps a constant distance from the tracked object, in this work the controller is specially designed in order to allow changes the tracking reference. Changes in the tracking reference can be utilized to grip an object that is under movement or as in this case, Ping-Pong playing. Lyapunov stability is taken into account in the controller design

Data-driven learning for robot physical intelligence

The physical intelligence, which emphasizes physical capabilities such as dexterous manipulation and dynamic mobility, is essential for robots to physically coexist with humans. Much research on robot physical intelligence has achieved success on hyper robot motor capabilities, but mostly through heavily case-specific engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous manner, robot learning from human demonstration (LfD) has achieved great progress, but still has limitations handling dynamic skills and compound actions. In this dissertation, a composite learning scheme which goes beyond LfD and integrates robot learning from human definition, demonstration, and evaluation is proposed. This method tackles advanced motor skills that require dynamic time-critical maneuver, complex contact control, and handling partly soft partly rigid objects. Besides, the power of crowdsourcing is brought to tackle case-specific engineering problem in the robot physical intelligence. Crowdsourcing has demonstrated great potential in recent development of artificial intelligence. Constant learning from a large group of human mentors breaks the limit of learning from one or a few mentors in individual cases, and has achieved success in image recognition, translation, and many other cyber applications. A robot learning scheme that allows a robot to synthesize new physical skills using knowledge acquired from crowdsourced human mentors is proposed. The work is expected to provide a long-term and big-scale measure to produce advanced robot physical intelligence

Computational modeling of impact and deformation

This thesis tackles several problems arising in robotics and mechanics: analysis and computation of two- and muti-body impacts, planning a contact velocity for robotic batting, impact of an elastic rod onto a fixed foundation, robotic pickup of soft three-dimensional objects, and recovery of their gravity-free shapes. Impact is an event that lasts a very short period of time but generates a very large interaction force. Assuming Stronge’s energy-based restitution, a formal impulse-based analysis is presented for the collision of two rigid bodies at single contact point under Coulomb friction in three dimensions (3D). Based on this analysis, we describe a complete algorithm to take advantage of fast numerical integration and closed-form evaluation. For a simultaneous collision involving more than two bodies, we describe a general computational model for predicting its outcome. Based on the impact model, we then look into the task of planning an initial contact velocity between a bat and an in-flight object to send the latter to a target. In certain situations, a closed-form solution can be found, while in others, a bounding triangle algorithm of iterative nature can be employed. An alternative way of modeling impact is to consider the engaged objects to be elastic rather than rigid. A damped one-dimensional wave equation can model an elastic rod bouncing off the ground at a given initial velocity, under the influence of gravity. We derive an explicit solution based on the Method of Descent and D’Alembert’s formula. We also obtain formulas for the time of contact and analyze the dependence of the energetic coefficient of restitution on the physical constants. I conclude the thesis with two pieces of work involving deformable objects. First, an algorithm for picking up a 3D object is introduced. Homotopy continuation method is applied to solve a non-linear system for slips between objects and fingers. Some simulation and experimental results are compared. Second, I discuss an iterative fixed-point method for recovering the gravity-free shape of an object. An experiment shows that the resulting stiffness matrix gives better predictions on deformations than the conventional stiffness matrix influenced by gravity

相対座標における高速視覚フィードバックに基づくダイナミックコンペンセーション

学位の種別:課程博士University of Tokyo(東京大学

Trajectory solutions for a game-playing robot using nonprehensile manipulation methods and machine vision

The need for autonomous systems designed to play games, both strategy-based and physical, comes from the quest to model human behaviour under tough and competitive environments that require human skill at its best. In the last two decades, and especially after the 1996 defeat of the world chess champion by a chess-playing computer, physical games have been receiving greater attention. Robocup TM, i.e. robotic football, is a well-known example, with the participation of thousands of researchers all over the world. The robots created to play snooker/pool/billiards are placed in this context. Snooker, as well as being a game of strategy, also requires accurate physical manipulation skills from the player, and these two aspects qualify snooker as a potential game for autonomous system development research. Although research into playing strategy in snooker has made considerable progress using various artificial intelligence methods, the physical manipulation part of the game is not fully addressed by the robots created so far. This thesis looks at the different ball manipulation options snooker players use, like the shots that impart spin to the ball in order to accurately position the balls on the table, by trying to predict the ball trajectories under the action of various dynamic phenomena, such as impacts. A 3-degree of freedom robot, which can manipulate the snooker cue on a par with humans, at high velocities, using a servomotor, and position the snooker cue on the ball accurately with the help of a stepper drive, is designed and fabricated. [Continues.