1,163 research outputs found

    A Visuo-Tactile Control Framework for Manipulation and Exploration of Unknown Objects

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    Li Q, Haschke R, Ritter H. A Visuo-Tactile Control Framework for Manipulation and Exploration of Unknown Objects. Presented at the Humanoids2015, Seoul,Korea.We present a novel hierarchical control framework that unifies our previous work on tactile-servoing with visual-servoing approaches to allow for robust manipulation and exploration of unknown objects, including – but not limited to – robust grasping, online grasp optimization, in-hand manipulation, and exploration of object surfaces. The control framework is divided into three layers: a joint-level positioncontrol layer, a tactile servoing control layer, and a high-level visual servoing control layer. While the middle layer provides “blind” surface exploration skills, maintaining desired contact patterns, the visual layer monitors and controls the actual object pose providing high-level finger-tip motion commands that are merged with the tactile-servoing control commands. Because the high spatial resolution tactile array and tactile servoing method is used, the robot end-effector can actively perform slide, roll and twist motion in order to improve the contact quality with the unknown object only depending on the tactile feedback. Our control method can be consider as another alternative option for vision-force shared control method and vision-force-tactile control method which heavily depend on the 3D force/torque sensor to perform end-effector fine manipulation after the contact happening. We illustrate the efficiency of the proposed framework using a series of manipulation actions performed with two KUKA LWR arms equipped with a tactile sensor array as a “sensitive fingertip”. The two considered objects are unknown to the robot, i.e. neither shape nor friction properties are available

    Push to know! -- Visuo-Tactile based Active Object Parameter Inference with Dual Differentiable Filtering

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    For robotic systems to interact with objects in dynamic environments, it is essential to perceive the physical properties of the objects such as shape, friction coefficient, mass, center of mass, and inertia. This not only eases selecting manipulation action but also ensures the task is performed as desired. However, estimating the physical properties of especially novel objects is a challenging problem, using either vision or tactile sensing. In this work, we propose a novel framework to estimate key object parameters using non-prehensile manipulation using vision and tactile sensing. Our proposed active dual differentiable filtering (ADDF) approach as part of our framework learns the object-robot interaction during non-prehensile object push to infer the object's parameters. Our proposed method enables the robotic system to employ vision and tactile information to interactively explore a novel object via non-prehensile object push. The novel proposed N-step active formulation within the differentiable filtering facilitates efficient learning of the object-robot interaction model and during inference by selecting the next best exploratory push actions (where to push? and how to push?). We extensively evaluated our framework in simulation and real-robotic scenarios, yielding superior performance to the state-of-the-art baseline.Comment: 8 pages. Accepted at IROS 202

    More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch

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    For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this paper, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp. To this end, we propose an end-to-end action-conditional model that learns regrasping policies from raw visuo-tactile data. This model -- a deep, multimodal convolutional network -- predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions. Our approach requires neither calibration of the tactile sensors, nor any analytical modeling of contact forces, thus reducing the engineering effort required to obtain efficient grasping policies. We train our model with data from about 6,450 grasping trials on a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. Across extensive experiments, our approach outperforms a variety of baselines at (i) estimating grasp adjustment outcomes, (ii) selecting efficient grasp adjustments for quick grasping, and (iii) reducing the amount of force applied at the fingers, while maintaining competitive performance. Finally, we study the choices made by our model and show that it has successfully acquired useful and interpretable grasping behaviors.Comment: 8 pages. Published on IEEE Robotics and Automation Letters (RAL). Website: https://sites.google.com/view/more-than-a-feelin

    Tactile Mapping and Localization from High-Resolution Tactile Imprints

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    This work studies the problem of shape reconstruction and object localization using a vision-based tactile sensor, GelSlim. The main contributions are the recovery of local shapes from contact, an approach to reconstruct the tactile shape of objects from tactile imprints, and an accurate method for object localization of previously reconstructed objects. The algorithms can be applied to a large variety of 3D objects and provide accurate tactile feedback for in-hand manipulation. Results show that by exploiting the dense tactile information we can reconstruct the shape of objects with high accuracy and do on-line object identification and localization, opening the door to reactive manipulation guided by tactile sensing. We provide videos and supplemental information in the project's website http://web.mit.edu/mcube/research/tactile_localization.html.Comment: ICRA 2019, 7 pages, 7 figures. Website: http://web.mit.edu/mcube/research/tactile_localization.html Video: https://youtu.be/uMkspjmDbq

    Visuo-Haptic Grasping of Unknown Objects through Exploration and Learning on Humanoid Robots

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    Die vorliegende Arbeit befasst sich mit dem Greifen unbekannter Objekte durch humanoide Roboter. Dazu werden visuelle Informationen mit haptischer Exploration kombiniert, um Greifhypothesen zu erzeugen. Basierend auf simulierten Trainingsdaten wird außerdem eine Greifmetrik gelernt, welche die Erfolgswahrscheinlichkeit der Greifhypothesen bewertet und die mit der größten geschätzten Erfolgswahrscheinlichkeit auswählt. Diese wird verwendet, um Objekte mit Hilfe einer reaktiven Kontrollstrategie zu greifen. Die zwei Kernbeiträge der Arbeit sind zum einen die haptische Exploration von unbekannten Objekten und zum anderen das Greifen von unbekannten Objekten mit Hilfe einer neuartigen datengetriebenen Greifmetrik

    Dexterous manipulation of unknown objects using virtual contact points

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    The manipulation of unknown objects is a problem of special interest in robotics since it is not always possible to have exact models of the objects with which the robot interacts. This paper presents a simple strategy to manipulate unknown objects using a robotic hand equipped with tactile sensors. The hand configurations that allow the rotation of an unknown object are computed using only tactile and kinematic information, obtained during the manipulation process and reasoning about the desired and real positions of the fingertips during the manipulation. This is done taking into account that the desired positions of the fingertips are not physically reachable since they are located in the interior of the manipulated object and therefore they are virtual positions with associated virtual contact points. The proposed approach was satisfactorily validated using three fingers of an anthropomorphic robotic hand (Allegro Hand), with the original fingertips replaced by tactile sensors (WTS-FT). In the experimental validation, several everyday objects with different shapes were successfully manipulated, rotating them without the need of knowing their shape or any other physical property.Peer ReviewedPostprint (author's final draft

    Self-Supervised Visuo-Tactile Pretraining to Locate and Follow Garment Features

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    Humans make extensive use of vision and touch as complementary senses, with vision providing global information about the scene and touch measuring local information during manipulation without suffering from occlusions. While prior work demonstrates the efficacy of tactile sensing for precise manipulation of deformables, they typically rely on supervised, human-labeled datasets. We propose Self-Supervised Visuo-Tactile Pretraining (SSVTP), a framework for learning multi-task visuo-tactile representations in a self-supervised manner through cross-modal supervision. We design a mechanism that enables a robot to autonomously collect precisely spatially-aligned visual and tactile image pairs, then train visual and tactile encoders to embed these pairs into a shared latent space using cross-modal contrastive loss. We apply this latent space to downstream perception and control of deformable garments on flat surfaces, and evaluate the flexibility of the learned representations without fine-tuning on 5 tasks: feature classification, contact localization, anomaly detection, feature search from a visual query (e.g., garment feature localization under occlusion), and edge following along cloth edges. The pretrained representations achieve a 73-100% success rate on these 5 tasks.Comment: RSS 2023, site: https://sites.google.com/berkeley.edu/ssvt

    VIRDO++: Real-World, Visuo-tactile Dynamics and Perception of Deformable Objects

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    Deformable objects manipulation can benefit from representations that seamlessly integrate vision and touch while handling occlusions. In this work, we present a novel approach for, and real-world demonstration of, multimodal visuo-tactile state-estimation and dynamics prediction for deformable objects. Our approach, VIRDO++, builds on recent progress in multimodal neural implicit representations for deformable object state-estimation [1] via a new formulation for deformation dynamics and a complementary state-estimation algorithm that (i) maintains a belief over deformations, and (ii) enables practical real-world application by removing the need for privileged contact information. In the context of two real-world robotic tasks, we show:(i) high-fidelity cross-modal state-estimation and prediction of deformable objects from partial visuo-tactile feedback, and (ii) generalization to unseen objects and contact formations
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