相关论文: General Hand Guidance Framework using Microsoft Ho…

Dexterous manipulation is a fundamental capability for robotic systems, yet progress has been limited by hardware trade-offs between precision, compactness, strength, and affordability. Existing control methods impose compromises on hand…

This publication reports on a research project in which we set out to explore the advantages and disadvantages augmented reality (AR) technology has for visual data analytics. We developed a prototype of an AR data analytics application,…

Traditional industrial robot programming is often complex and time-consuming, typically requiring weeks or even months of effort from expert programmers. Although Programming by Demonstration (PbD) offers a more accessible alternative,…

Recent years have seen a surge in commercially-available and affordable quadrupedal robots, with many of these platforms being actively used in research and industry. As the availability of legged robots grows, so does the need for…

This paper describes a global framework that enables contactless human machine interaction using computer vision and machine learning techniques. The main originality of our framework is that only a very simple image acquisition device, as…

This paper studies real-time collaborative robot (cobot) handling, where the cobot maneuvers an object under human dynamic gesture commands. Enabling dynamic gesture commands is useful when the human needs to avoid direct contact with the…

Hand gestures recognition (HGR) is one of the main areas of research for the engineers, scientists and bioinformatics. HGR is the natural way of Human Machine interaction and today many researchers in the academia and industry are working…

Creating a diverse and comprehensive dataset of hand gestures for dynamic human-machine interfaces in the automotive domain can be challenging and time-consuming. To overcome this challenge, we propose using synthetic gesture datasets…

We tackle the problem of highly-accurate, holistic performance capture for the face, body and hands simultaneously. Motion-capture technologies used in film and game production typically focus only on face, body or hand capture…

Robotic manipulation in unstructured environments requires end-effectors that combine high kinematic dexterity with physical compliance. While traditional rigid hands rely on complex external sensors for safe interaction, electrohydraulic…

Most current anthropomorphic robotic hands can realize part of the human hand functions, particularly for object grasping. However, due to the complexity of the human hand, few current designs target at daily object manipulations, even for…

We propose a real-time vision-based teleoperation approach for robotic arms that employs a single depth-based camera, exempting the user from the need for any wearable devices. By employing a natural user interface, this novel approach…

This paper presents a vision-based learning-by-demonstration approach to enable robots to learn and complete a manipulation task cooperatively. With this method, a vision system is involved in both the task demonstration and reproduction…

Tool use is essential for enabling robots to perform complex real-world tasks, but learning such skills requires extensive datasets. While teleoperation is widely used, it is slow, delay-sensitive, and poorly suited for dynamic tasks. In…

Collaborative robots can relief human operators from excessive efforts during payload lifting activities. Modelling the human partner allows the design of safe and efficient collaborative strategies. In this paper, we present a control…

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…

Recent robot learning methods commonly rely on imitation learning from massive robotic dataset collected with teleoperation. When facing a new task, such methods generally require collecting a set of new teleoperation data and finetuning…

Learning from demonstration (LfD) has the potential to greatly increase the applicability of robotic manipulators in modern industrial applications. Recent progress in LfD methods have put more emphasis in learning robustness than in…

Hand detection is essential for many hand related tasks, e.g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction. However, hand detection in uncontrolled environments is…

This paper introduces MobileH2R, a framework for learning generalizable vision-based human-to-mobile-robot (H2MR) handover skills. Unlike traditional fixed-base handovers, this task requires a mobile robot to reliably receive objects in a…