The iCub multisensor datasets for robot and computer vision applications

• URL: http://arxiv.org/abs/2003.01994v1
• Date: Wed, 4 Mar 2020 10:59:29 GMT
• Title: The iCub multisensor datasets for robot and computer vision applications
• Authors: Murat Kirtay, Ugo Albanese, Lorenzo Vannucci, Guido Schillaci, Cecilia Laschi, Egidio Falotico
• Abstract summary: This document presents novel datasets constructed by employing the iCub robot equipped with an additional depth sensor and color camera. We used the robot to acquire color and depth information for 210 objects in different acquisition scenarios.
• Score: 0.7340017786387767
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This document presents novel datasets, constructed by employing the iCub robot equipped with an additional depth sensor and color camera. We used the robot to acquire color and depth information for 210 objects in different acquisition scenarios. At this end, the results were large scale datasets for robot and computer vision applications: object representation, object recognition and classification, and action recognition.

Related papers

• Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction [52.12746368727368]
  Differentiable simulation has become a powerful tool for system identification. Our approach calibrates object properties by using information from the robot, without relying on data from the object itself. We demonstrate the effectiveness of our method on a low-cost robotic platform.
  arXiv  Detail & Related papers  (2024-10-04T20:48:38Z)
• Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models [53.22792173053473]
  We introduce an interactive robotic manipulation framework called Polaris. Polaris integrates perception and interaction by utilizing GPT-4 alongside grounded vision models. We propose a novel Synthetic-to-Real (Syn2Real) pose estimation pipeline.
  arXiv  Detail & Related papers  (2024-08-15T06:40:38Z)
• Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots [22.63980025871784]
  The proposed framework integrates heterogeneous data streams collected from various robot sensors, including RGB and depth cameras, a microphone, and a force-torque sensor. The integrated data is used to train a deep autoencoder to construct latent representations of the multisensory data that indicate the normal status. Anomalies can then be identified by error scores measured by the difference between the trained encoder's latent values and the latent values of reconstructed input data.
  arXiv  Detail & Related papers  (2024-03-06T09:15:53Z)
• Teaching Unknown Objects by Leveraging Human Gaze and Augmented Reality in Human-Robot Interaction [3.1473798197405953]
  This dissertation aims to teach a robot unknown objects in the context of Human-Robot Interaction (HRI) The combination of eye tracking and Augmented Reality created a powerful synergy that empowered the human teacher to communicate with the robot. The robot's object detection capabilities exhibited comparable performance to state-of-the-art object detectors trained on extensive datasets.
  arXiv  Detail & Related papers  (2023-12-12T11:34:43Z)
• Polybot: Training One Policy Across Robots While Embracing Variability [70.74462430582163]
  We propose a set of key design decisions to train a single policy for deployment on multiple robotic platforms. Our framework first aligns the observation and action spaces of our policy across embodiments via utilizing wrist cameras. We evaluate our method on a dataset collected over 60 hours spanning 6 tasks and 3 robots with varying joint configurations and sizes.
  arXiv  Detail & Related papers  (2023-07-07T17:21:16Z)
• HabitatDyn Dataset: Dynamic Object Detection to Kinematics Estimation [16.36110033895749]
  We propose the dataset HabitatDyn, which contains both synthetic RGB videos, semantic labels, and depth information, as well as kinetics information. HabitatDyn was created from the perspective of a mobile robot with a moving camera, and contains 30 scenes featuring six different types of moving objects with varying velocities.
  arXiv  Detail & Related papers  (2023-04-21T09:57:35Z)
• RT-1: Robotics Transformer for Real-World Control at Scale [98.09428483862165]
  We present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks.
  arXiv  Detail & Related papers  (2022-12-13T18:55:15Z)
• Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions [82.90906153293585]
  We propose a graph neural network, HO-GCN, to fuse motion data and dynamic descriptors for the prediction task. We show the proposed network that consumes dynamic descriptors can achieve state-of-the-art prediction results and help the network better generalize to unseen objects.
  arXiv  Detail & Related papers  (2022-06-25T09:55:39Z)
• MetaGraspNet: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis [78.26022688167133]
  We present a large-scale benchmark dataset for vision-driven robotic grasping via physics-based metaverse synthesis. The proposed dataset contains 100,000 images and 25 different object types. We also propose a new layout-weighted performance metric alongside the dataset for evaluating object detection and segmentation performance.
  arXiv  Detail & Related papers  (2021-12-29T17:23:24Z)
• Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging [14.458436940557924]
  We present a multimodal sensing technique, leveraging near-infrared spectroscopy and close-range high resolution texture imaging. We show that this representation enables a robot to recognize materials with greater performance as compared to prior state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-04-02T17:33:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.