Gaining a Sense of Touch. Physical Parameters Estimation using a Soft Gripper and Neural Networks

• URL: http://arxiv.org/abs/2003.00784v2
• Date: Tue, 3 Mar 2020 16:30:37 GMT
• Title: Gaining a Sense of Touch. Physical Parameters Estimation using a Soft Gripper and Neural Networks
• Authors: Micha{\l} Bednarek, Piotr Kicki, Jakub Bednarek, Krzysztof Walas
• Abstract summary: There is not enough research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers. We propose a trainable system for the regression of a stiffness coefficient and provided extensive experiments using the physics simulator environment. Our system can reliably estimate the stiffness of an object using the Yale OpenHand soft gripper based on readings from Inertial Measurement Units (IMUs) attached to its fingers.
• Score: 3.0892724364965005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects which are vulnerable to deformations. A crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which is considered as a significant challenge. To the best of the authors' knowledge, there is not enough research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers. In our work, we proposed a trainable system for the regression of a stiffness coefficient and provided extensive experiments using the physics simulator environment. Moreover, we prepared the application that works in the real-world scenario. Our system can reliably estimate the stiffness of an object using the Yale OpenHand soft gripper based on readings from Inertial Measurement Units (IMUs) attached to its fingers. Additionally, during the experiments, we prepared three datasets of signals gathered while squeezing objects -- two created in the simulation environment and one composed of real data.

Related papers

• Differentiable Physics-based System Identification for Robotic Manipulation of Elastoplastic Materials [43.99845081513279]
  This work introduces a novel Differentiable Physics-based System Identification (DPSI) framework that enables a robot arm to infer the physics parameters of elastoplastic materials and the environment. With only a single real-world interaction, the estimated parameters can accurately simulate visually and physically realistic behaviours.
  arXiv  Detail & Related papers  (2024-11-01T13:04:25Z)
• Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems [49.11170948406405]
  State-of-the-art in automatic parameter estimation from video is addressed by training supervised deep networks on large datasets. We propose a method to estimate the physical parameters of any known, continuous governing equation from single videos.
  arXiv  Detail & Related papers  (2024-10-02T09:44:54Z)
• RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing [38.97168020979433]
  We introduce an approach that combines visual and tactile sensing for robotic manipulation by learning a neural, tactile-informed dynamics model. Our proposed framework, RoboPack, employs a recurrent graph neural network to estimate object states. We demonstrate our approach on a real robot equipped with a compliant Soft-Bubble tactile sensor on non-prehensile manipulation and dense packing tasks.
  arXiv  Detail & Related papers  (2024-07-01T16:08:37Z)
• DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation [81.11585774044848]
  We present DeepSimHO, a novel deep-learning pipeline that combines forward physics simulation and backward gradient approximation with a neural network. Our method noticeably improves the stability of the estimation and achieves superior efficiency over test-time optimization.
  arXiv  Detail & Related papers  (2023-10-11T05:34:36Z)
• Reconfigurable Data Glove for Reconstructing Physical and Virtual Grasps [100.72245315180433]
  We present a reconfigurable data glove design to capture different modes of human hand-object interactions. The glove operates in three modes for various downstream tasks with distinct features. We evaluate the system's three modes by (i) recording hand gestures and associated forces, (ii) improving manipulation fluency in VR, and (iii) producing realistic simulation effects of various tool uses.
  arXiv  Detail & Related papers  (2023-01-14T05:35:50Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Transformer Inertial Poser: Attention-based Real-time Human Motion Reconstruction from Sparse IMUs [79.72586714047199]
  We propose an attention-based deep learning method to reconstruct full-body motion from six IMU sensors in real-time. Our method achieves new state-of-the-art results both quantitatively and qualitatively, while being simple to implement and smaller in size.
  arXiv  Detail & Related papers  (2022-03-29T16:24:52Z)
• Information-Theoretic Odometry Learning [83.36195426897768]
  We propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation. The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language.
  arXiv  Detail & Related papers  (2022-03-11T02:37:35Z)
• Learning suction graspability considering grasp quality and robot reachability for bin-picking [4.317666242093779]
  We propose an intuitive geometric analytic-based grasp quality evaluation metric. We further incorporate a reachability evaluation metric. Experiment results show that our intuitive grasp quality evaluation metric is competitive with a physically-inspired metric.
  arXiv  Detail & Related papers  (2021-11-04T00:55:42Z)
• Leveraging distributed contact force measurements for slip detection: a physics-based approach enabled by a data-driven tactile sensor [5.027571997864706]
  This paper describes a novel model-based slip detection pipeline that can predict possibly failing grasps in real-time. A vision-based tactile sensor that accurately estimates distributed forces was integrated into a grasping setup composed of a six degrees-of-freedom cobot and a two-finger gripper. Results show that the system can reliably predict slip while manipulating objects of different shapes, materials, and weights.
  arXiv  Detail & Related papers  (2021-09-23T17:12:46Z)

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.