Related papers: DexEMG: Towards Dexterous Teleoperation System via…
We introduce an efficient approach for learning dexterous grasping with minimal data, advancing robotic manipulation capabilities across different robotic hands. Unlike traditional methods that require millions of grasp labels for each…
In sensitive scenarios, such as meetings, negotiations, and team sports, messages must be conveyed without detection by non-collaborators. Previous methods, such as encrypting messages, eye contact, and micro-gestures, had problems with…
Hand gesture recognition (HGR) has gained significant attention due to the increasing use of AI-powered human-computer interfaces that can interpret the deep spatiotemporal dynamics of biosignals from the peripheral nervous system, such as…
Teleoperation is a key paradigm for transferring human dexterity to robots, yet most prior work targets objects that are initially static, such as grasping or manipulation. Dynamic object catch, where objects move before contact, remains…
This study introduces a novel language-guided diffusion-based learning framework, DexTOG, aimed at advancing the field of task-oriented grasping (TOG) with dexterous hands. Unlike existing methods that mainly focus on 2-finger grippers,…
This paper explores the possibility of improving bilateral robot manipulation task performance through optimizing the robot morphology and configuration of the system through motion. To optimize the design for different scenarios, we select…
Hand gesture recognition using multichannel surface electromyography (sEMG) is challenging due to unstable predictions and inefficient time-varying feature enhancement. To overcome the lack of signal based time-varying feature problems, we…
Generating dexterous grasping has been a long-standing and challenging robotic task. Despite recent progress, existing methods primarily suffer from two issues. First, most prior arts focus on a specific type of robot hand, lacking the…
Hand exoskeletons are critical tools for dexterous teleoperation and immersive manipulation interfaces, but achieving accurate hand tracking remains a challenge due to user-specific anatomical variability and donning inconsistencies. These…
Robotic-assisted procedures offer enhanced precision, but while fully autonomous systems are limited in task knowledge, difficulties in modeling unstructured environments, and generalisation abilities, fully manual teleoperated systems also…
Human life is invaluable. When dangerous or life-threatening tasks need to be completed, robotic platforms could be ideal in replacing human operators. Such a task that we focus on in this work is the Explosive Ordnance Disposal. Robot…
There has been a surge of recent interest in Machine Learning (ML), particularly Deep Neural Network (DNN)-based models, to decode muscle activities from surface Electromyography (sEMG) signals for myoelectric control of neurorobotic…
Teleoperation provides an effective way to collect robot data, which is crucial for learning from demonstrations. In this field, teleoperation faces several key challenges: user-friendliness for new users, safety assurance, and…
Wearable robotic hand rehabilitation devices can allow greater freedom and flexibility than their workstation-like counterparts. However, the field is generally lacking effective methods by which the user can operate the device: such…
Generalizable grasping with high-degree-of-freedom (DoF) dexterous hands remains challenging in tiered workspaces, where occlusion, narrow clearances, and height-dependent constraints are substantially stronger than in open tabletop scenes.…
The aim of this work was to identify six basic movements of the hand using two systems. Being an interdisciplinary topic, there has been conducted studying in the anatomy of forearm muscles, biosignals, the method of electromyography (EMG)…
In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands. Robot joint angles are directly generated from depth images of the human hand that…
Dexterous robot hand teleoperation allows for long-range transfer of human manipulation expertise, and could simultaneously provide a way for humans to teach these skills to robots. However, current methods struggle to reproduce the…
Recent advancements in teleoperation systems have enabled high-quality data collection for robotic manipulators, showing impressive results in learning manipulation at scale. This progress suggests that extending these capabilities to…
Developing accurate hand gesture perception models is critical for various robotic applications, enabling effective communication between humans and machines and directly impacting neurorobotics and interactive robots. Recently, surface…