Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater

• URL: http://arxiv.org/abs/2308.08510v1
• Date: Wed, 16 Aug 2023 17:07:37 GMT
• Title: Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater
• Authors: Ning Guo, Xudong Han, Xiaobo Liu, Shuqiao Zhong, Zhiyuan Zhou, Jian Lin, Jiansheng Dai, Fang Wan, Chaoyang Song
• Abstract summary: This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger. A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learned a series of latent representations of the soft mechanics transferrable from land to water.
• Score: 17.27917150366665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, mainly due to the fluidic interference on the tactile mechanics between the finger and object surfaces. This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger that learns 6D forces and torques (FT) using a Supervised Variational Autoencoder (SVAE). A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learned a series of latent representations of the soft mechanics transferrable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. Soft, delicate, and reactive grasping enabled by tactile intelligence enhances the gripper's underwater interaction with improved reliability and robustness at a much-reduced cost, paving the path for learning-based intelligent grasping to support fundamental scientific discoveries in environmental and ocean research.

Related papers

• Learning Underwater Active Perception in Simulation [51.205673783866146]
  Turbidity can jeopardise the whole mission as it may prevent correct visual documentation of the inspected structures. Previous works have introduced methods to adapt to turbidity and backscattering. We propose a simple yet efficient approach to enable high-quality image acquisition of assets in a broad range of water conditions.
  arXiv  Detail & Related papers  (2025-04-23T06:48:38Z)
• Robust Object Detection of Underwater Robot based on Domain Generalization [1.6317061277457001]
  The diversity and complexity of underwater environments bring new challenges to object detection. The various water quality and changeable and extreme lighting conditions lead to the distorted, low contrast, blue or green images. This paper investigates the problems brought by the underwater environment and aims to design a high-performance and robust underwater object detector.
  arXiv  Detail & Related papers  (2025-03-18T22:01:26Z)
• Stonefish: Supporting Machine Learning Research in Marine Robotics [5.021710505685786]
  This paper highlights recent enhancements to the Stonefish simulator, an open-source platform supporting development and testing of marine robotics solutions. Key updates include a suite of additional sensors, as well as, visual light communication, support for tethered operations, improved thruster modelling, more flexible hydrodynamics, and enhanced sonar accuracy.
  arXiv  Detail & Related papers  (2025-02-17T15:13:41Z)
• Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring [68.41400824104953]
  This paper presents a vehicle prototype that addresses the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. By means of a stereo-camera, it also can detect and locate macro-plastics in real environments.
  arXiv  Detail & Related papers  (2024-10-08T10:35:32Z)
• A transformer-based deep reinforcement learning approach to spatial navigation in a partially observable Morris Water Maze [0.0]
  This work applies a transformer-based architecture using deep reinforcement learning to navigate a 2D version of the Morris Water Maze. We demonstrate that the proposed architecture enables the agent to efficiently learn spatial navigation strategies. This work suggests promising avenues for future research in artificial agents whose behavior resembles that of biological agents.
  arXiv  Detail & Related papers  (2024-10-01T13:22:56Z)
• Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions [9.392180262607921]
  Reinforcement learning-based quadruped robots excel across various terrains but still lack the ability to swim in water. This paper presents the development and evaluation of a data-driven hydrodynamic model for amphibious quadruped robots.
  arXiv  Detail & Related papers  (2024-10-01T08:18:36Z)
• Floor extraction and door detection for visually impaired guidance [78.94595951597344]
  Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people.
  arXiv  Detail & Related papers  (2024-01-30T14:38:43Z)
• Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration [79.76009817889397]
  We propose a robust learning policy based on Learning from Demonstration (LfD) for robotic grasping of food compliant objects. We present an LfD learning policy that automatically removes inconsistent demonstrations, and estimates the teacher's intended policy. The proposed approach has a vast range of potential applications in the aforementioned industry sectors.
  arXiv  Detail & Related papers  (2023-09-22T13:30:26Z)
• Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
  A fully-autonomous aerial robot for high-speed object grasping has been proposed. As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface. Our approach has been validated in a challenging international competition and has shown outstanding results.
  arXiv  Detail & Related papers  (2021-12-10T11:49:51Z)
• Elastic Tactile Simulation Towards Tactile-Visual Perception [58.44106915440858]
  We propose Elastic Interaction of Particles (EIP) for tactile simulation. EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact. We further propose a tactile-visual perception network that enables information fusion between tactile data and visual images.
  arXiv  Detail & Related papers  (2021-08-11T03:49:59Z)
• Learning Intuitive Physics with Multimodal Generative Models [24.342994226226786]
  This paper presents a perception framework that fuses visual and tactile feedback to make predictions about the expected motion of objects in dynamic scenes. We use a novel See-Through-your-Skin (STS) sensor that provides high resolution multimodal sensing of contact surfaces. We validate through simulated and real-world experiments in which the resting state of an object is predicted from given initial conditions.
  arXiv  Detail & Related papers  (2021-01-12T12:55:53Z)
• DARE: AI-based Diver Action Recognition System using Multi-Channel CNNs for AUV Supervision [3.5584173777587935]
  This paper presents DARE, a diver action recognition system that is trained based on Cognitive Autonomous Driving Buddy dataset. DARE is fast and requires only a few milliseconds to classify one stereo-pair, thus making it suitable for real-time underwater implementation.
  arXiv  Detail & Related papers  (2020-11-16T04:05:32Z)
• ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation [75.0278287071591]
  ThreeDWorld (TDW) is a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. We present initial experiments enabled by TDW in emerging research directions in computer vision, machine learning, and cognitive science.
  arXiv  Detail & Related papers  (2020-07-09T17:33:27Z)
• DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation [16.54834671357377]
  General purpose in-hand manipulation remains one of the unsolved challenges of robotics. We introduce DIGIT, an inexpensive, compact, and high-resolution tactile sensor geared towards in-hand manipulation.
  arXiv  Detail & Related papers  (2020-05-29T17:07:54Z)

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.