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Multi-Scale Embodied Intelligence Lab
Intelligent Robotics Across Scales
email: d.zhang17@imperial.ac.uk
Intelligent Dexterous Manipulation
Here shows our work related to Dexterous Manipulation with AI.
Affordable Robotic Hands for Dexterous Manipulation:
We're making significant efforts to explore cost-effective yet durable materials to construct affordable robotic hands. This hand can provide high-quality robotic assistance accessible to all, regardless of economic constraints. By exploring cost-effective yet durable materials, we aim to reduce the overall manufacturing costs without compromising the longevity or performance of the robotic hands. We aim to investigate dexterous in-hand manipulation, ensuring the robotic hand can perform a wide array of tasks from picking up delicate objects to exerting significant force when necessary. We will investigate User-Centric Customizations with Modular designs that allow users to tailor the robotic hand to specific needs.
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TacFR-Gripper: A Reconfigurable Fin Ray-Based Compliant Robotic Gripper
with Tactile Skin for In-Hand Manipulation
This paper introduces the TacFR-Gripper, a reconfigurable Fin Ray-based soft and compliant robotic gripper equipped with tactile skin, which can be used for dexterous in-hand manipulation tasks. This gripper can adaptively grasp objects of diverse shapes and stiffness levels. An array of Force Sensitive Resistor (FSR) sensors is embedded within the robotic finger to serve as the tactile skin, enabling the robot to perceive contact information during manipulation. We provide theoretical analysis for gripper design, including kinematic analysis, workspace analysis, and finite element analysis to identify the relationship between the gripper's load and its deformation. Moreover, we implemented a Graph Neural Network (GNN)-based tactile perception approach to enable reliable grasping without accidental slip or excessive force.
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Three physical experiments were conducted to quantify the performance of the TacFR-Gripper. These experiments aimed to i) assess the grasp success rate across various everyday objects through different configurations, ii) verify the effectiveness of tactile skin with the GNN algorithm in grasping, iii) evaluate the gripper's in-hand manipulation capabilities for object pose control.
The experimental results indicate that the TacFR-Gripper can grasp a wide range of complex-shaped objects with a high success rate and deliver dexterous in-hand manipulation. Additionally, the integration of tactile skin with the GNN algorithm enhances grasp stability by incorporating tactile feedback during manipulations.
