Physical human-robot collaboration: Robotic systems, learning methods, collaborative strategies, sensors, and actuators

This article presents a state-of-the-art survey on the robotic systems, sensors, actuators, and collaborative strategies for physical human-robot collaboration (pHRC). This article starts with an overview of some robotic systems with cutting-edge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed the relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances is made, some research directions, and future challenges are presented

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

Towards observable haptics: Novel sensors for capturing tactile interaction patterns

Kõiva R. Towards observable haptics: Novel sensors for capturing tactile interaction patterns. Bielefeld: Bielefeld University; 2014.Touch is one of the primary senses humans use when performing coordinated interaction, but the lack of a sense of touch in the majority of contemporary interactive technical systems, such as robots, which operate in non-deterministic environments, results in interactions that can at best be described as clumsy. Observing human haptics and extracting the salient information from the gathered data is not only relevant if we are to try to understand the involved underlying cognitive processes, but should also provide us with significant clues to design future intelligent interactive systems. Such systems could one day help to take the burden of tedious tasks off our hands in a similar fashion to how industrial robots revolutionized manufacturing. The aim of the work in this thesis was to provide significant advancements in tactile sensing technology, and thus move us a step closer to realizing this goal. The contributions contained herein can be broken into two major parts. The first part investigates capturing interaction patterns in humans with the goals of better understanding manual intelligence and improving the lives of hand amputees, while the second part is focused on augmenting technical systems with a sense of touch. tacTiles, a wireless tactile sensitive surface element attached to a deformable textile, was developed to capture human full-body interactions with large surfaces we come into contact with in our daily lives, such as floors, chairs, sofas or other furniture. The Tactile Dataglove, iObject and the Tactile Pen were developed especially to observe human manual intelligence. Whereas iObject allows motion sensing and a higher definition tactile signal to be captured than the Tactile Dataglove (220 tactile cells in the first iObject prototype versus 54 cells in the glove), the wearable glove makes haptic interactions with arbitrary objects observable. The Tactile Pen was designed to measure grip force during handwriting in order to better facilitate therapeutic treatment assessments. These sensors have already been extensively used by various research groups, including our own, to gain a better understanding of human manual intelligence. The Finger-Force-Linear-Sensor and the Tactile Bracelet are two novel sensors that were developed to facilitate more natural control of dexterous multi Degree-of-Freedom (DOF) hand prostheses. The Finger-Force-Linear-Sensor is a very accurate bidirectional single finger force ground-truth measurement device that was designed to enable testing and development of single finger forces and muscle activations mapping algorithms. The Tactile Bracelet was designed with the goal to provide a more robust and intuitive means of control for multi-DOF hand prostheses by measuring the muscle bulgings of the remnant muscles of lower arm amputees. It is currently in development and will eventually cover the complete forearm circumference with high spatial resolution tactile sensitive surfaces. An experiment involving a large number of lower arm amputees has already been planned. The Modular flat tactile sensor system, the Fabric-based touch sensitive artificial skin and the 3D shaped tactile sensor were developed to cover and to add touch sensing capabilities to the surfaces of technical systems. The rapid augmentation of systems with a sense of touch was the main goal of the modular flat tactile sensor system. The developed sensor modules can be used alone or in an array to form larger tactile sensitive surfaces such as tactile sensitive tabletops. As many robots have curved surfaces, using flat rigid modules severely limits the areas that can be covered with tactile sensors. The Fabric-based tactile sensor, originally developed to form a tactile dataglove for human hands, can with minor modifications also function as an artificial skin for technical systems. Finally, the 3D shaped tactile sensor based on Laser-Direct-Structuring technology is a novel tactile sensor that has a true 3D shape and provides high sensitivity and a high spatial resolution. These sensors take us further along the path towards creating general purpose technical systems that in time can be of great help to us in our daily lives. The desired tactile sensor characteristics differ significantly according to which haptic interaction patterns we wish to measure. Large tactile sensor arrays that are used to capture full body haptic interactions with floors and upholstered furniture, or that are designed to cover large areas of technical system surfaces, need to be scalable, have low power consumption and should ideally have a low material cost. Two examples of such sensors are tacTiles and the Fabric-based sensor for curved surfaces. At the other end of the tactile sensor development spectrum, if we want to observe manual interactions, high spatial and temporal resolution are crucial to enable the measurement of fine grasping and manipulation actions. Our fingertips contain the highest density area of mechanoreceptors, the organs that sense mechanical pressure and distortions. Thus, to construct biologically inspired anthropomorphic robotic hands, the artificial tactile sensors for the fingertips require similar high-fidelity sensors with surfaces that are curved under small bending radii in 2 dimensions, have high spatial densities, while simultaneously providing high sensitivity. With the fingertip tactile sensor, designed to fit the Shadow Robot Hands' fingers, I show that such sensors can indeed be constructed in the 3D-shaped high spatial resolution tactile sensor section of my thesis. With my work I have made a significant contribution towards making haptics more observable. I achieved this by developing a high number of novel tactile sensors that are usable, give a deeper insight into human haptic interactions, have great potential to help amputees and that make technical systems, such as robots, more capable

Utilizing Compliance To Address Modern Challenges in Robotics

Mechanical compliance will be an essential component for agile robots as they begin to leave the laboratory settings and join our world. The most crucial finding of this dissertation is showing how lessons learned from soft robotics can be adapted into traditional robotics to introduce compliance. Therefore, it presents practical knowledge on how to build soft bodied sensor and actuation modules: first example being soft-bodied curvature sensors. These sensors contain both standard electronic components soldered on flexible PCBs and hyperelastic materials that cover the electronics. They are built by curing multi-material composites inside hyper elastic materials. Then it shows, via precise sensing by using magnets and Hall-effect sensors, how closed-loop control of soft actuation modules can be achieved via proprioceptive feedback. Once curvature sensing idea is verified, the dissertation describes how the same sensing methodology, along with the same multi-material manufacturing technique can be utilized to construct soft bodied tri-axial force sensors. It shows experimentally that these sensors can be used by traditional robotic grippers to increase grasping quality. At this point, I observe that compliance is an important property that robots may utilize for different types of motions. One example being Raibert\u27s 2D hopper mechanism. It uses its leg-spring to store energy while on the ground and release this energy before jumping. I observe that via soft material design, it would be possible to embed compliance directly into the linkage design itself. So I go over the design details of an extremely lightweight compliant five-bar mechanism design that can store energy when compressed via soft ligaments embedded in its joints. I experimentally show that the compliant leg design offers increased efficiency compared to a rigid counterpart. I also utilize the previously mentioned soft bodied force sensors for rapid contact detection (~5-10 Hz) in the hopper test platform. In the end, this thesis connects soft robotics with the traditional body of robotic knowledge in two aspects: a) I show that manufacturing techniques we use for soft bodied sensor/actuator designs can be utilized for creating soft ligaments that add strength and compliance to robot joints; and b) I demonstrate that soft bodied force sensing techniques can be used reliably for robotic contact detection

Tactile Perception And Visuotactile Integration For Robotic Exploration

As the close perceptual sibling of vision, the sense of touch has historically received less than deserved attention in both human psychology and robotics. In robotics, this may be attributed to at least two reasons. First, it suffers from the vicious cycle of immature sensor technology, which causes industry demand to be low, and then there is even less incentive to make existing sensors in research labs easy to manufacture and marketable. Second, the situation stems from a fear of making contact with the environment, avoided in every way so that visually perceived states do not change before a carefully estimated and ballistically executed physical interaction. Fortunately, the latter viewpoint is starting to change. Work in interactive perception and contact-rich manipulation are on the rise. Good reasons are steering the manipulation and locomotion communities’ attention towards deliberate physical interaction with the environment prior to, during, and after a task. We approach the problem of perception prior to manipulation, using the sense of touch, for the purpose of understanding the surroundings of an autonomous robot. The overwhelming majority of work in perception for manipulation is based on vision. While vision is a fast and global modality, it is insufficient as the sole modality, especially in environments where the ambient light or the objects therein do not lend themselves to vision, such as in darkness, smoky or dusty rooms in search and rescue, underwater, transparent and reflective objects, and retrieving items inside a bag. Even in normal lighting conditions, during a manipulation task, the target object and fingers are usually occluded from view by the gripper. Moreover, vision-based grasp planners, typically trained in simulation, often make errors that cannot be foreseen until contact. As a step towards addressing these problems, we present first a global shape-based feature descriptor for object recognition using non-prehensile tactile probing alone. Then, we investigate in making the tactile modality, local and slow by nature, more efficient for the task by predicting the most cost-effective moves using active exploration. To combine the local and physical advantages of touch and the fast and global advantages of vision, we propose and evaluate a learning-based method for visuotactile integration for grasping

Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots

積算状態推定に基づくヒューマノイドロボットの継続的タスク実行システムの構成法

学位の種別: 課程博士審査委員会委員 : （主査）東京大学准教授 岡田 慧, 東京大学教授 中村 仁彦, 東京大学教授 稲葉 雅幸, 東京大学教授 國吉 康夫, 東京大学准教授 高野 渉University of Tokyo(東京大学