Visuo-Haptic Object Perception for Robots: An Overview
- URL: http://arxiv.org/abs/2203.11544v1
- Date: Tue, 22 Mar 2022 08:55:36 GMT
- Title: Visuo-Haptic Object Perception for Robots: An Overview
- Authors: Nicol\'as Navarro-Guerrero, Sibel Toprak, Josip Josifovski, Lorenzo
Jamone
- Abstract summary: This article summarizes the current state of multimodal object perception for robotic applications.
It covers aspects of biological inspiration, sensor technologies, data sets, and sensory data processing for object recognition and grasping.
- Score: 1.7033055327465234
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This article summarizes the current state of multimodal object perception for
robotic applications. It covers aspects of biological inspiration, sensor
technologies, data sets, and sensory data processing for object recognition and
grasping. Firstly, the biological basis of multimodal object perception is
outlined. Then the sensing technologies and data collection strategies are
discussed. Next, an introduction to the main computational aspects is
presented, highlighting a few representative articles for each main application
area, including object recognition, object manipulation and grasping, texture
recognition, and transfer learning. Finally, informed by the current
advancements in each area, this article outlines promising new research
directions.
Related papers
- Deepfake Generation and Detection: A Benchmark and Survey [134.19054491600832]
Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions.
This survey comprehensively reviews the latest developments in deepfake generation and detection.
We focus on researching four representative deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing.
arXiv Detail & Related papers (2024-03-26T17:12:34Z) - The ObjectFolder Benchmark: Multisensory Learning with Neural and Real
Objects [51.22194706674366]
We introduce the Object Benchmark, a benchmark suite of 10 tasks for multisensory object-centric learning.
We also introduce the Object Real dataset, including the multisensory measurements for 100 real-world household objects.
arXiv Detail & Related papers (2023-06-01T17:51:22Z) - A Comprehensive Study on Object Detection Techniques in Unconstrained
Environments [0.0]
Object detection is a crucial task in computer vision that aims to identify and localize objects in images or videos.
The recent advancements in deep learning and Convolutional Neural Networks (CNNs) have significantly improved the performance of object detection techniques.
This paper presents a comprehensive study of object detection techniques in unconstrained environments, including various challenges, datasets, and state-of-the-art approaches.
arXiv Detail & Related papers (2023-04-11T15:45:03Z) - Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey [71.10448142010422]
Multi-object tracking (MOT) aims to associate target objects across video frames in order to obtain entire moving trajectories.
Embedding methods play an essential role in object location estimation and temporal identity association in MOT.
We first conduct a comprehensive overview with in-depth analysis for embedding methods in MOT from seven different perspectives.
arXiv Detail & Related papers (2022-05-22T06:54:33Z) - 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) - Roadmap on Signal Processing for Next Generation Measurement Systems [0.222020259427608]
Recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing.
This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems.
It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field.
arXiv Detail & Related papers (2021-11-03T19:39:34Z) - ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and
Tactile Representations [52.226947570070784]
We present Object, a dataset of 100 objects that addresses both challenges with two key innovations.
First, Object encodes the visual, auditory, and tactile sensory data for all objects, enabling a number of multisensory object recognition tasks.
Second, Object employs a uniform, object-centric simulations, and implicit representation for each object's visual textures, tactile readings, and tactile readings, making the dataset flexible to use and easy to share.
arXiv Detail & Related papers (2021-09-16T14:00:59Z) - Capturing the objects of vision with neural networks [0.0]
Human visual perception carves a scene at its physical joints, decomposing the world into objects.
Deep neural network (DNN) models of visual object recognition, by contrast, remain largely tethered to the sensory input.
We review related work in both fields and examine how these fields can help each other.
arXiv Detail & Related papers (2021-09-07T21:49:53Z) - Simultaneous Multi-View Object Recognition and Grasping in Open-Ended
Domains [0.0]
We propose a deep learning architecture with augmented memory capacities to handle open-ended object recognition and grasping simultaneously.
We demonstrate the ability of our approach to grasp never-seen-before objects and to rapidly learn new object categories using very few examples on-site in both simulation and real-world settings.
arXiv Detail & Related papers (2021-06-03T14:12:11Z) - A Review on Intelligent Object Perception Methods Combining
Knowledge-based Reasoning and Machine Learning [60.335974351919816]
Object perception is a fundamental sub-field of Computer Vision.
Recent works seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects.
arXiv Detail & Related papers (2019-12-26T13:26:49Z)
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.