Browsing by Author "Calandra, Roberto"

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  • ItemOpen Access
    Learning Gentle Grasping Using Vision, Sound, and Touch
    (Technische Universität Dresden, 2025-03-11) Nakahara, Ken; Calandra, Roberto
    This dataset contains 1,500 robotic grasps collected for the paper of Learning Gentle Grasping Using Vision, Sound, and Touch. Additionally, we provide a description of this dataset and Python scripts to visualize the data and process raw data into a training dataset for a PyTorch model. The robotics system used consists of a multi-fingered robotic hand (16-DoF, Allegro Hand v4.0), 7-DoF robotic arms (xArm7), DIGIT tactile sensors, an RGB-D camera (Intel RealSense D435i), and a commodity microphone. The target object is a toy that emits sound when grasped strongly.
  • ItemOpen Access
    Temporal Visuo-Tactile Learning for Dexterous Grasp Stability
    (Technische Universität Dresden, 2026-10-08) Nakahara, Ken; Buvailik, Aleksei; Kotov, Prokhor; Calandra, Roberto
    This dataset contains 10,000 multimodal robotic grasp trials across 200 everyday objects with diverse shapes, sizes, materials, and compliance, collected for the paper Temporal Visuo-Tactile Learning for Dexterous Grasp Stability. The platform combines a 7-DoF xArm7, a four-fingered, 16-DoF Tilburg Hand with a Digit 360 tactile sensor on each fingertip, and an external RealSense D435i RGB-D camera. Each trial continuously records external RGB-D observations, arm and hand proprioception, tactile images, audio, inertial measurements, pressure, and commanded actions throughout the full grasp attempt, from reaching and hand closure to lifting and post-lift outcome checking. The recordings include phase annotations and binary grasp-stability labels indicating whether the object was successfully lifted and held for three seconds without dropping. Timestamped, asynchronously recorded observations and actions, together with phase start times, allow selected intervals to be resampled into synchronized multimodal sequences; the pre-lift window used in the paper is one such example. The release also provides object reference images, per-object metadata, and Python tools for data visualization and resampling into training-ready samples. These full-grasp recordings support reuse beyond the prediction setting studied in the paper, including action-conditional grasp stability prediction, contact dynamics modeling, and multimodal representation learning for dexterous manipulation.
  • ItemOpen Access
    The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?
    (Technische Universität Dresden, 2025-02-16) Calandra, Roberto; Owens, Andrew; Upadhyaya, Manu; Yuan, Wenzhen; Lin, Justin; Adelson, Edward H.; Levine, Sergey
    This dataset contains 9269 robotic grasps collected from 106 objects, and their corresponding outcome. The hardware used consists of a 7-DoF Sawyer arm, a Weiss WSG-50 parallel gripper, one Microsoft Kinect 2, and two GelSight sensors, one for each finger.