Nonprehensile Manipulation of Deformable Objects: Achievements and Perspectives from the RobDyMan Project

International audienceThe goal of this work is to disseminate the results achieved so far within the RODYMAN project related to planning and control strategies for robotic nonprehensile manipulation. The project aims at advancing the state of the art of nonprehensile dynamic manipulation of rigid and deformable objects to future enhance the possibility of employing robots in anthropic environments. The final demonstrator of the RODYMAN project will be an autonomous pizza maker. This article is a milestone to highlight the lessons learned so far and pave the way towards future research directions and critical discussions

Reactive Motions In A Fully Autonomous CRS Catalyst 5 Robotic Arm Based On RGBD Data

This study proposes a method to perform velocity estimation using motion blur in a single image frame along x and y axes in the camera coordinate system and intercept a moving object with a robotic arm. It will be shown that velocity estimation in a single image frame improves the system\u27s performance. The majority of previous studies in this area require at least two image frames to measure the target\u27s velocity. In addition, they mostly employ specialized equipments which are able to generate high torques and accelerations. The setup consists of a 5 degree of freedom robotic arm and a Kinect camera. The RGBD (Red, Green, Blue and Depth) camera provides the RGB and depth information which are used to detect the position of the target. As the object is moving within a single image frame, the image contains motion blur. To recognize and differentiate the object from blurred area, the image intensity profiles are studied. Accordingly, the method determines the blur parameters based on the changes in the intensity profile. The aforementioned blur parameters are the length of the object and the length of the partial blur. Based on motion blur, the velocities along x and y camera coordinate axes are estimated. However, as the depth frame cannot record motion blur, the velocity along axis in the camera coordinate frame is initially unknown. The vectors of position and velocity are transformed into world coordinate frame and subsequently, the prospective position of the object, after a predefined time interval, is predicted. In order to intercept, the end-effector of the robotic arm must reach this predicted position within the time interval as well. For the end-effector to reach the predicted position within the predefined time interval, the robot\u27s joint angles and accelerations are determined through inverse kinematic methods. Then the robotic arm starts its motion. Once the second depth frame is obtained, the object\u27s velocity along z axis can be calculated as well. Accordingly, the predicted position of the object is recalculated, and the motion of the manipulator is modified. The proposed method is compared with existing methods which need at least two image frames to estimate the velocity of the target. It is shown that under identical kinematic conditions, the functionality of the system is improved by times for our setup. In addition, the experiment is repeated for times and the velocity data is recorded. According to the experimental results, there are two major limitations in our system and setup. The system cannot determine the velocity along z in the camera coordinate system from the initial image frame. Consequently, if the object travels faster along this axis, it becomes more susceptible to failure. In addition, our manipulator is an unspecialized equipment which is not designed for producing high torques and accelerations. Accordingly, the task becomes more challenging. The main cause of error in the experiments was operator\u27s. It is necessary to have the object pass through the working volume of the robot. Besides, the object must be still inside the working volume after the predefined time interval. It is possible that the operator throw the object within the designated working volume, but it leaves it earlier than the specified time interval

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

Estimating the non-linear dynamics of free-flying objects

This paper develops a model-free method to estimate the dynamics of free-flying objects. We take a realistic perspective to the problem and investigate tracking accurately and very rapidly the trajectory and orientation of an object so as to catch it in flight. We consider the dynamics of complex objects where the grasping point is not located at the center of mass. To achieve this, a density estimate of the translational and rotational velocity is built based on the trajectories of various examples. We contrast the performance of six non-linear regression methods (Support Vector Regression (SVR) with Radial Basis Function (RBF) kernel, SVR with polynomial kernel, Gaussian Mixture Regression (GMR), Echo State Network (ESN), Genetic Programming (GP) and Locally Weighted Projection Regression (LWPR)) in terms of precision of recall, computational cost and sensitivity to choice of hyper-parameters. We validate the approach for real-time motion tracking of 5 daily life objects with complex dynamics (a ball, a fully-filled bottle, a half-filled bottle, a hammer and a pingpong racket). To enable real-time tracking, the estimated model of the object's dynamics is coupled with an Extended Kalman Filter for robustness against noisy sensing. (C) 2012 Elsevier B.V. All rights reserved

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

DESIGN AND EVALUATION OF A NONVERBAL COMMUNICATION PLATFORM BETWEEN ASSISTIVE ROBOTS AND THEIR USERS

Assistive robotics will become integral to the everyday lives of a human population that is increasingly mobile, older, urban-centric and networked. The overwhelming demands on healthcare delivery alone will compel the adoption of assistive robotics. How will we communicate with such robots, and how will they communicate with us? This research makes the case for a relatively \u27artificial\u27 mode of nonverbal human-robot communication that is non-disruptive, non-competitive, and non-invasive human-robot communication that we envision will be willingly invited into our private and working lives over time. This research proposes a non-verbal communication (NVC) platform be conveyed by familiar lights and sounds, and elaborated here are experiments with our NVC platform in a rehabilitation hospital. This NVC is embedded into the Assistive Robotic Table (ART), developed within our lab, that supports the well-being of an expanding population of older adults and those with limited mobility. The broader aim of this research is to afford people robot-assistants that exist and interact with them in the recesses, rather than in the foreground, of their intimate and social lives. With support from our larger research team, I designed and evaluated several alternative modes of nonverbal robot communication with the objective of establishing a nonverbal, human-robot communication loop that evolves with users and can be modified by users. The study was conducted with 10-13 clinicians -- doctors and occupational, physical, and speech therapists -- at a local rehabilitation hospital through three iterative design and evaluation phases and a final usability study session. For our test case at a rehabilitation hospital, medical staff iteratively refined our NVC platform, stated a willingness to use our platform, and declared NVC as a desirable research path. In addition, these clinicians provided the requirements for human-robot interaction (HRI) in clinical settings, suggesting great promise for our mode of human-robot communication for this and other applications and environments involving intimate HRI