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Object Identification And Pose Estimation Using Bio Inspired Tactile Enabled Multi Joint Fingers For In Hand Manipulation
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Book Synopsis Object Identification and Pose Estimation Using Bio-Inspired Tactile-Enabled Multi-Joint Fingers for In-Hand Manipulation by : Vinicius Prado da Fonseca
Download or read book Object Identification and Pose Estimation Using Bio-Inspired Tactile-Enabled Multi-Joint Fingers for In-Hand Manipulation written by Vinicius Prado da Fonseca and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In-hand manipulation is a major challenge that has to be addressed in order for robots to achieve human-like skills and manipulation abilities. A new generation of humanoid robots will need dexterous hands able to deal with uncertainties, especially when they are expected to operate in unstructured environments, such as homes and hospitals. Given the human ability to quickly obtain and understand tactile data, one promising direction in order to achieve enhanced robotic dexterous skills is to investigate and emulate human manipulation capabilities. In humans, a combination of somatosensory subsystems deals with everyday manipulation tasks. This thesis introduces a new approach for estimating the pose of a grasped object by combining tactile sensing data and visual frames of reference inspired by the human "Where" subsystem. While tactile sensing produces local data about objects during in-hand manipulation, a vision system generates egocentric and allocentric frames of reference. Object recognition in the early grasp phases in unstructured environments is also a fundamental ability for robots to achieve human-level manipulation skills. Humans developed a so-called "haptic glance" where non-exploratory manipulation perform fast object identification. Tactile sensors contribute useful information about the objects manipulated by robots, especially during in-hand operations. Drawing inspiration from the functionality of the "What" somatosensory pathway, the proposed solution uses machine learning methods to recognize objects in the early phases of manipulation. The thesis describes innovative work on object recognition using data collected from bio-inspired multi-modal tactile sensing modules in static and dynamic tasks. The system takes advantage of the module's compliant structure and inertial, magnetic and pressure measurements. During all experiments, a dual fuzzy logic controller autonomously achieves and maintains stable grasping conditions while forces applied to in-hand objects expose the tactile system to various object configurations. This thesis also presents results on simultaneous object characterization during exploratory procedures using teleoperation.
Book Synopsis Human Hand Pose Estimation and Artificial Tactile Sensing in Harsh Environments by : Eric Peltola
Download or read book Human Hand Pose Estimation and Artificial Tactile Sensing in Harsh Environments written by Eric Peltola and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Study #1, we develop a novel, data-driven method to estimate the kinematic parameters of multi-joint linkages such as the human hand. The method can identify up to two consecutive revolute joint axis orientations between connected rigid bodies. We introduce kinematic constraints into a Generative Topographic Mapping formulation in order to estimate the joint axis parameters. The method was evaluated using simulated motion and via motion capture and a physical 2-DOF mechanism modeled after the metacarpophalangeal joint of the human finger. Our method compares well against state-of-the-art kinematic parameter estimation techniques with regards to reliability and computational efficiency. In Study #2, we introduce a sensor-embedded soft skin capable of multimodal sensing (contact force and two axes of shear force) in pressurized underwater environments up to 1000kPa. We embed liquid-metal strain gauges within a durable elastomeric skin that is molded around a solid finger core. We demonstrate that the sensor skin is capable of measuring forces up to 220N underwater and while subjected to a range of hydrostatic pressures. We determine that the performance of the sensor skin is unaffected by the submerged, pressurized environment. In Study #3, we propose a set of design considerations for tactile sensor skins using embedded, microfluidic single-axis strain gauges for the purpose of estimating 3D forces and 1D torque about the skin's surface normal. By displacing shear force taxels such that their principal axes are offset from the point of contact, we are able to more accurately measure torque. We use an experimental testbed to apply force-torque loads to the sensor skin. We develop CNN-based models to evaluate the combined force-torque estimation performance of numerous taxel configurations and provide a detailed discussion of how performance relates to design choices. In summary, we developed methods that improve sensing in harsh environments such as granular media and underwater. Kinematic and kinetic considerations during hand-object interaction were carefully integrated into the development of novel sensing hardware, data-driven estimation methods, and task-specific sensor design criteria. We advanced the state-of-the-art in tactile sensing using tactile sensor skins and improved the accuracy of hand pose estimation using low-cost motion tracking tools.
Book Synopsis Object Surface Exploration Using a Tactile-Enabled Robotic Fingertip by : Bruno Monteiro Rocha Lima
Download or read book Object Surface Exploration Using a Tactile-Enabled Robotic Fingertip written by Bruno Monteiro Rocha Lima and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploring surfaces is an essential ability for humans, allowing them to interact with a large variety of objects within their environment. This ability to explore surfaces is also of a major interest in the development of a new generation of humanoid robots, which requires the development of more efficient artificial tactile sensing techniques. The details perceived by statically touching different surfaces of objects not only improve robotic hand performance in force-controlled grasping tasks but also enables the feeling of vibrations on touched surfaces. This thesis presents an extensive experimental study of object surface exploration using biologically-Inspired tactile-enabled robotic fingers. A new multi-modal tactile sensor, embedded in both versions of the robotic fingertips (similar to the human distal phalanx) is capable of measuring the heart rate with a mean absolute error of 1.47 bpm through static explorations of the human skin. A two-phalanx articulated robotic finger with a new miniaturized tactile sensor embedded into the fingertip was developed in order to detect and classify surface textures. This classification is performed by the dynamic exploration of touched object surfaces. Two types of movements were studied: one-dimensional (1D) and two-dimensional (2D) movements. The machine learning techniques - Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forest, Extra Trees, and k-Nearest Neighbors (kNN) - were tested in order to find the most efficient one for the classification of the recovered textured surfaces. A 95% precision was achieved when using the Extra Trees technique for the classification of the 1D recovered texture patterns. Experimental results confirmed that the 2D textured surface exploration using a hemispheric tactile-enabled finger was superior to the 1D exploration. Three exploratory velocities were used for the 2D exploration: 30 mm/s, 35 mm/s, and 40 mm/s. The best classification accuracy of the 2D recovered texture patterns was 99.1% and 99.3%, using the SVM classifier, for the two lower exploratory velocities (30 mm/s and 35mm/s), respectively. For the 40 mm/s velocity, the Extra Trees classifier provided a classification accuracy of 99.4%. The results of the experimental research presented in this thesis could be suitable candidates for future development.
Book Synopsis The Human Hand as an Inspiration for Robot Hand Development by : Ravi Balasubramanian
Download or read book The Human Hand as an Inspiration for Robot Hand Development written by Ravi Balasubramanian and published by Springer. This book was released on 2014-01-03 with total page 573 pages. Available in PDF, EPUB and Kindle. Book excerpt: “The Human Hand as an Inspiration for Robot Hand Development” presents an edited collection of authoritative contributions in the area of robot hands. The results described in the volume are expected to lead to more robust, dependable, and inexpensive distributed systems such as those endowed with complex and advanced sensing, actuation, computation, and communication capabilities. The twenty-four chapters discuss the field of robotic grasping and manipulation viewed in light of the human hand’s capabilities and push the state-of-the-art in robot hand design and control. Topics discussed include human hand biomechanics, neural control, sensory feedback and perception, and robotic grasp and manipulation. This book will be useful for researchers from diverse areas such as robotics, biomechanics, neuroscience, and anthropologists.
Book Synopsis Touch Based Object Pose Estimation for Robotic Grasping by : Joao Bimbo
Download or read book Touch Based Object Pose Estimation for Robotic Grasping written by Joao Bimbo and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot grasping and manipulation require very accurate and timely knowledge of the manipulated object's shape and pose to successfully perform a desired task. One of the main reasons current systems fail to carry out complex tasks in a real, unstructured environment is their inability to accurately determine where in, the object the fingers are touching. Most systems use vision to detect the pose of an object, but the performance of this sensing modality deteriorates as soon as the robot grasps the object. When the robot hand contacts an object, it partially occludes it, which makes it difficult for vision systems to track the object's location. This thesis presents algorithms to use the robot's available tactile sensing to correct the visually determined pose of a grasped object. This method is extended to globally estimate the pose of the object even when no initial estimate is given. Two different tactile sensing strategies have been employed: single-point and distributed, and measurement models for these two strategies are presented. Different optimisation algorithms are developed and tested to minimise the output of these measurement models and find one or more poses that satisfy current tactile measurements. Results show that the method is able to successfully estimate the pose of a grasped object with high accuracy, even for objects with a high degree of geometric complexity. Other applications of the method are proposed, such as determining grasp stability or identifying the grasped object, as well as future research directions.
Book Synopsis In-hand Robotic Tactile Object Recognition by : Alex Vásquez
Download or read book In-hand Robotic Tactile Object Recognition written by Alex Vásquez and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotic anthropomorphic hands are mostly used to reproduce the human dexterity in manipulation. Beyond the mechanical and control challenges that this represents, perceptive knowledge of the environment with which the hand interacts is key to ensure that dexterity is achieved. In this sense, tactile object recognition has become an important asset for manipulation systems. Regardless of the advances in this domain, it continues to be a valid subject of research today. In this thesis, we propose a method to enable a robotic hand to quickly understand the geometrical nature of an object that has been handled by it. Aside from the static data obtained once the object has been fully grasped, the movements of the hand during the grasp execution will also be exploited. As a first contribution, we propose the proprioceptive shape signature. This descriptor, based solely on proprioceptive data, is invariant to the size and pose of the object within the hand and it contains information about the global shape of the object almost as soon as the grasp execution ends. As a second contribution, we propose a tool to extract information about the grasped object from the dynamic data generated during the grasp execution. For this, the movements of the fingers during the grasping process will be interpreted based on the grasp strategy. Finally, we present a method to perform sequential object shape identification based on a collection of random forests. This method allows to update the recognition model as new shapes are desired to be identified. Thus, the time-consuming process of training the model from scratch is avoided.
Book Synopsis Estimation and Inference for Grasping and Manipulation Tasks Using Vision and Kinesthetic Sensors by : Paul Hebert
Download or read book Estimation and Inference for Grasping and Manipulation Tasks Using Vision and Kinesthetic Sensors written by Paul Hebert and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a novel framework for state estimation in the context of robotic grasping and manipulation. The overall estimation approach is based on fusing various visual cues for manipulator tracking, namely appearance and feature-based, shape-based, and silhouette-based visual cues. Similarly, a framework is developed to fuse the above visual cues, but also kinesthetic cues such as force-torque and tactile measurements, for in-hand object pose estimation. The cues are extracted from multiple sensor modalities and are fused in a variety of Kalman filters. A hybrid estimator is developed to estimate both a continuous state (robot and object states) and discrete states, called contact modes, which specify how each finger contacts a particular object surface. A static multiple model estimator is used to compute and maintain this mode probability. The thesis also develops an estimation framework for estimating model parameters associated with object grasping. Dual and joint state-parameter estimation is explored for parameter estimation of a grasped object's mass and center of mass. Experimental results demonstrate simultaneous object localization and center of mass estimation. Dual-arm estimation is developed for two arm robotic manipulation tasks. Two types of filters are explored; the first is an augmented filter that contains both arms in the state vector while the second runs two filters in parallel, one for each arm. These two frameworks and their performance is compared in a dual-arm task of removing a wheel from a hub. This thesis also presents a new method for action selection involving touch. This next best touch method selects an available action for interacting with an object that will gain the most information. The algorithm employs information theory to compute an information gain metric that is based on a probabilistic belief suitable for the task. An estimation framework is used to maintain this belief over time. Kinesthetic measurements such as contact and tactile measurements are used to update the state belief after every interactive action. Simulation and experimental results are demonstrated using next best touch for object localization, specifically a door handle on a door. The next best touch theory is extended for model parameter determination. Since many objects within a particular object category share the same rough shape, principle component analysis may be used to parametrize the object mesh models. These parameters can be estimated using the action selection technique that selects the touching action which best both localizes and estimates these parameters. Simulation results are then presented involving localizing and determining a parameter of a screwdriver. Lastly, the next best touch theory is further extended to model classes. Instead of estimating parameters, object class determination is incorporated into the information gain metric calculation. The best touching action is selected in order to best discern between the possible model classes. Simulation results are presented to validate the theory.
Book Synopsis Biomimetic Tactile Sensor for Object Identification and Grip Control by : Nicholas Wettels
Download or read book Biomimetic Tactile Sensor for Object Identification and Grip Control written by Nicholas Wettels and published by LAP Lambert Academic Publishing. This book was released on 2011-10 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: We have developed a finger-shaped sensor array (BioTac) that provides simultaneous information about the contact forces, microvibrations and thermal fluxes induced by contact with external objects. For tasks such as identifying objects or maintaining stable grasp, these sensory modalities tend to be synergistic. For example, information about texture and slip can be derived from vibrations of skin ridges sliding over a surface, but only if the forces on the skin are known and well-controlled. Similarly, information about the material composition of an object can be inferred from the rate of heat transfer from a heated finger to the object, but only if the location and force of contact are similarly well-controlled. The BioTAC sensor is intrinsically simple, robust, and easy to manufacture and repair. The skin possesses texture and tackiness similar to the properties of human skin that facilitate grip and can be easily replaced if worn or damaged. The curved, deformable nature of biological finger tips provides mechanical features that are important for the manipulation of the wide variety of objects encountered naturally.
Book Synopsis Towards an In-hand Object Monitoring and Object Pose Estimation in Robotics Featuring Compliant, High-resolution Tactile Sensors by : Veit Müller
Download or read book Towards an In-hand Object Monitoring and Object Pose Estimation in Robotics Featuring Compliant, High-resolution Tactile Sensors written by Veit Müller and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis HandyPose and VehiPose by : Divyansh Gupta
Download or read book HandyPose and VehiPose written by Divyansh Gupta and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Pose estimation is an important and challenging task in computer vision. Hand pose estimation has drawn increasing attention during the past decade and has been utilized in a wide range of applications including augmented reality, virtual reality, human-computer interaction, and action recognition. Hand pose is more challenging than general human body pose estimation due to the large number of degrees of freedom and the frequent occlusions of joints. To address these challenges, we propose HandyPose, a single-pass, end-to-end trainable architecture for hand pose estimation. Adopting an encoder-decoder framework with multi-level features, our method achieves high accuracy in hand pose while maintaining manageable size complexity and modularity of the network. HandyPose takes a multi-scale approach to representing context by incorporating spatial information at various levels of the network to mitigate the loss of resolution due to pooling. Our advanced multi-level waterfall architecture leverages the efficiency of progressive cascade filtering while maintaining larger fields-of-view through the concatenation of multi-level features from different levels of the network in the waterfall module. The decoder incorporates both the waterfall and multi-scale features for the generation of accurate joint heatmaps in a single stage. Recent developments in computer vision and deep learning have achieved significant progress in human pose estimation, but little of this work has been applied to vehicle pose. We also propose VehiPose, an efficient architecture for vehicle pose estimation, based on a multi-scale deep learning approach that achieves high accuracy vehicle pose estimation while maintaining manageable network complexity and modularity. The VehiPose architecture combines an encoder-decoder architecture with a waterfall atrous convolution module for multi-scale feature representation. It incorporates contextual information across scales and performs the localization of vehicle keypoints in an end-to-end trainable network. Our HandyPose architecture has a baseline of vehipose with an improvement in performance by incorporating multi-level features from different levels of the backbone and introducing novel multi-level modules. HandyPose and VehiPose more thoroughly leverage the image contextual information and deal with the issue of spatial loss of resolution due to successive pooling while maintaining the size complexity, modularity of the network, and preserve the spatial information at various levels of the network. Our results demonstrate state-of-the-art performance on popular datasets and show that HandyPose and VehiPose are robust and efficient architectures for hand and vehicle pose estimation."--Abstract.
Book Synopsis Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation by : Qiang Li
Download or read book Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation written by Qiang Li and published by Elsevier. This book was released on 2022-04-07 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: tactile sensing, skill learning and dexterous control. The book introduces recent work about human dexterous skill representation and learning, along with discussions of tactile sensing and its applications on unknown objects' property recognition and reconstruction. Sections also introduce the adaptive control schema and its learning by imitation and exploration. Other chapters describe the fundamental part of relevant research, paying attention to the connection among different fields and showing the state-of-the-art in related branches. The book summarizes the different approaches and discusses the pros and cons of each. Chapters not only describe the research but also include basic knowledge that can help readers understand the proposed work, making it an excellent resource for researchers and professionals who work in the robotics industry, haptics and in machine learning. Provides a review of tactile perception and the latest advances in the use of robotic dexterous manipulation Presents the most detailed work on synthesizing intelligent tactile perception, skill learning and adaptive control Introduces recent work on human's dexterous skill representation and learning and the adaptive control schema and its learning by imitation and exploration Reveals and illustrates how robots can improve dexterity by modern tactile sensing, interactive perception, learning and adaptive control approaches
Book Synopsis Tactile Based Object Recognition For Prosthetic Hands by : Abhijit Boruah
Download or read book Tactile Based Object Recognition For Prosthetic Hands written by Abhijit Boruah and published by Mohammed Abdul Sattar. This book was released on 2023-12-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Anthropomorphic prosthetic hands must resemble human hands concerning their kinematic abilities and significant features such as object recognition on grasp. Implementation of object recognition by prosthetic hands require prior information on the extrinsic structural properties of objects as well as the hand's position and orientation. Object recognition approaches are primarily of two categories: vision and tactile. Vision-based learning methods have dominated the realm of object recognition for robotic and prosthetic hands in the past decades. Relying only on vision is not sufficient for the perceptual requirements of a prosthetic hand. The human hand is a complex structure with multiple degrees of freedom (DoF), leading to various movements and grasp formations. Acquiring the full range of motion in the fingers and the wrist during prosthetic hand development is critical. Creating such dexterity involves an intense investigation to extract knowledge of motion and joint constraints in the phalanges and wrist bones. The increase of digital information in the current age has elevated the demands of semantically rich annotations for applications shared over the internet. In recent years, the popularity of philosophical knowledge representation methods like ontology to under- stand and utilize relevant domain concepts for problem specifications has escalated. Ontologies are significant in providing a meaningful schema by linking unstructured data. Object recognition during a grasp is an essential attribute in a prosthetic hand, which takes its development closer to its natural counterpart.
Book Synopsis Object Pose Estimation and Tracking with Deep Learning for Robot Manipulation by : Tao Chen
Download or read book Object Pose Estimation and Tracking with Deep Learning for Robot Manipulation written by Tao Chen and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Surface Estimation from Multi-modal Tactile Data by : Isura Thrikawala
Download or read book Surface Estimation from Multi-modal Tactile Data written by Isura Thrikawala and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing popularity of Robotic applications has seen use in healthcare, surgery, and as an industrial tool. These robots are expected to be able to make physical contact with the objects in the environment which allows tasks such as grasping and manipulation, while also allowing to obtain information about the objects such as shape, texture, and hardness. In an ideal world, a complete model of the environment would be known beforehand and robots would not need to explore objects and surfaces since their information would be available in the model of the world. In the real world, most environments are unstructured and robots must be able to operate safely without causing harm to themselves or objects while taking into account environmental uncertainties and building models for the environment and its objects. To overcome this, the trend has been to use computer vision to detect objects in the environment. Although computer vision has seen great advancement in this regard, there are some problems that cannot be solved by using vision alone. Objects that are occluded, transparent, or do not have rich visual features cannot be detected by using vision. It is also impossible to estimate features such as hardness or tactile texture using vision. To this end, we use a bio-inspired tactile sensor consisting of a compliant structure, a MARG sensor, and a pressure sensor along with a robotic manipulator to explore surfaces with the only assumption that the general location of the surface is known. This sensing module allows the robotic manipulator to have a predetermined angle of approach which is essential when exploring unseen surfaces. [...].
Book Synopsis Estimating Global Object Pose from Tactile Images by : Antonia Bronars
Download or read book Estimating Global Object Pose from Tactile Images written by Antonia Bronars and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work evaluates Tac2Pose, an object-specific approach to tactile pose estimation for known objects. Given the object geometry, we learn a perception model in simulation that estimates a probability distribution over possible object poses given a tactile observation. To do so, we simulate the contact shapes that a dense set of object poses would produce on the sensor. Then, given a new contact shape obtained from the sensor, we match it against the pre-computed set using an object-specific embedding learned using contrastive learning. We obtain contact shapes from the sensor with an object-agnostic calibration step that maps RGB tactile images to binary contact shapes. This mapping, which can be reused across object and sensor instances, is the only step trained with real sensor data. Tac2Pose produces pose distributions and can incorporate additional pose constraints coming from other perception systems, multiple contacts, or priors. We provide quantitative results for 20 objects. Tac2Pose provides high accuracy pose estimations from distinctive tactile observations while regressing meaningful pose distributions to account for those contact shapes that could result from different object poses. We test Tac2Pose in multi-contact scenarios where two tactile sensors are simultaneously in contact with the object, as during a grasp with a parallel jaw gripper. We further show that when the output pose distribution is filtered with a prior on the object pose, Tac2Pose is often able to improve significantly on the prior. This suggests synergistic use of Tac2Pose with additional sensing modalities (e.g. vision) even in cases where the tactile observation from a grasp is not sufficiently discriminative. Given a coarse estimate, even ambiguous contacts can be used to determine an object's pose precisely. We also test Tac2Pose on object models reconstructed from a 3D scanner, to evaluate the robustness to uncertainty in the object model. We show that even in the presence of model uncertainty, Tac2Pose is able to achieve fine accuracy comparable to when the object model is the manufacturer's CAD model. Finally, we demonstrate the advantages of Tac2Pose compared with three baseline methods for tactile pose estimation: directly regressing the object pose with a neural network, matching an observed contact to a set of possible contacts using a standard classification neural network, and direct pixel comparison of an observed contact with a set of possible contacts.
Book Synopsis Development of a Bio-inspired MEMS Based Tactile Sensor Array for an Artificial Finger by : Hassena Bashir Muhammad
Download or read book Development of a Bio-inspired MEMS Based Tactile Sensor Array for an Artificial Finger written by Hassena Bashir Muhammad and published by . This book was released on 2011 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis 2016 International Symposium on Experimental Robotics by : Dana Kulić
Download or read book 2016 International Symposium on Experimental Robotics written by Dana Kulić and published by Springer. This book was released on 2017-03-20 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experimental Robotics XV is the collection of papers presented at the International Symposium on Experimental Robotics, Roppongi, Tokyo, Japan on October 3-6, 2016. 73 scientific papers were selected and presented after peer review. The papers span a broad range of sub-fields in robotics including aerial robots, mobile robots, actuation, grasping, manipulation, planning and control and human-robot interaction, but shared cutting-edge approaches and paradigms to experimental robotics. The readers will find a breadth of new directions of experimental robotics. The International Symposium on Experimental Robotics is a series of bi-annual symposia sponsored by the International Foundation of Robotics Research, whose goal is to provide a forum dedicated to experimental robotics research. Robotics has been widening its scientific scope, deepening its methodologies and expanding its applications. However, the significance of experiments remains and will remain at the center of the discipline. The ISER gatherings are a venue where scientists can gather and talk about robotics based on this central tenet.
