ArticleSensors (Basel, Switzerland)2020
Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
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Who cites it
8 citing papers in PubMed.
- Bioinspired High-Resolution, Wide-Range Pressure Sensor Enabled by Synergistic CB/CNT Island-Chain and PVDF/PDMS Fiber Network for Dexterous Robotic Hands.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Calculation of Robot Multi-Fingered Grasping Force and Displacement Based on the Newton-Subgradient Non-Smooth Greedy Randomized Kaczmarz Method for Solving Linear Complementarity Problem.Sensors (Basel, Switzerland) · 2025Article
- Human-like Dexterous Grasping Through Reinforcement Learning and Multimodal Perception.Biomimetics (Basel, Switzerland) · 2025Article
- Universal slip detection of robotic hand with tactile sensing.Frontiers in neurorobotics · 2025Article
- Advancement in Soft Hydrogel Grippers: Comprehensive Insights into Materials, Fabrication Strategies, Grasping Mechanism, and Applications.Biomimetics (Basel, Switzerland) · 2024Review
- Robotics Perception and Control: Key Technologies and Applications.Micromachines · 2024Review
- Flexible 3D Force Sensor Based on Polymer Nanocomposite for Soft Robotics and Medical Applications.Sensors (Basel, Switzerland) · 2024Article
- Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively unexplored. In this paper, we make use of tactile sensing for multi-fingered robot hands to adjust the grasping force to stabilize unknown objects without prior knowledge of their shape or physical properties. In particular, an online detection module based on Deep Neural Network (DNN) is designed to detect contact events and object material simultaneously from tactile data. In addition, a force estimation method based on Gaussian Mixture Model (GMM) is proposed to compute the contact information (i.e., contact force and contact location) from tactile data. According to the results of tactile sensing, an object stabilization controller is then employed for a robotic hand to adjust the contact configuration for object stabilization. The spatio-temporal property of tactile data is exploited during tactile sensing. Finally, the effectiveness of the proposed framework is evaluated in a real-world experiment with a five-fingered Shadow Dexterous Hand equipped with BioTac sensors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.