相关论文: DOGlove: Dexterous Manipulation with a Low-Cost Op…

Cardiopulmonary resuscitation (CPR) is a critical life-saving procedure, and effective training benefits from self-directed practice beyond instructor-led sessions. In this paper, we propose a closed-loop CPR training glove that integrates…

This paper introduces RoboDexVLM, an innovative framework for robot task planning and grasp detection tailored for a collaborative manipulator equipped with a dexterous hand. Previous methods focus on simplified and limited manipulation…

Human-robot collaboration has benefited users with higher efficiency towards interactive tasks. Nevertheless, most collaborative schemes rely on complicated human-machine interfaces, which might lack the requisite intuitiveness compared…

Large-scale, diverse robot datasets have emerged as a promising path toward enabling dexterous manipulation policies to generalize to novel environments, but acquiring such datasets presents many challenges. While teleoperation provides…

Recently, haptic gloves have been extensively explored for various practical applications, such as manipulation learning. Previous glove devices have different force-driven systems, such as shape memory alloys, servo motors and pneumatic…

A fundamental challenge in teaching robots is to provide an effective interface for human teachers to demonstrate useful skills to a robot. This challenge is exacerbated in dexterous manipulation, where teaching high-dimensional,…

Contact feedback is essential for contact-rich robotic manipulation, as it allows the robot to detect subtle interaction changes and adjust its actions accordingly. Six-axis force-torque sensors are commonly used to obtain contact feedback,…

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…

High cost and lack of reliability has precluded the widespread adoption of dexterous hands in robotics. Furthermore, the lack of a viable tactile sensor capable of sensing over the entire area of the hand impedes the rich, low-level…

Dexterous multi-fingered hands can accomplish fine manipulation behaviors that are infeasible with simple robotic grippers. However, sophisticated multi-fingered hands are often expensive and fragile. Low-cost soft hands offer an appealing…

The inherent difficulty and limited scalability of collecting manipulation data using multi-fingered robot hand hardware platforms have resulted in severe data scarcity, impeding research on data-driven dexterous manipulation policy…

This paper presents ReGlove, a system that converts low-cost commercial pneumatic rehabilitation gloves into vision-guided assistive orthoses. Chronic upper-limb impairment affects millions worldwide, yet existing assistive technologies…

Human hand actions are quite complex, especially when they involve object manipulation, mainly due to the high dimensionality of the hand and the vast action space that entails. Imitating those actions with dexterous hand models involves…

Teleoperation offers a promising solution for enabling hands-on learning in remote education, particularly in environments requiring interaction with real-world equipment. However, such remote experiences can be costly or non-intuitive. To…

The use of wearable robots has been widely adopted in rehabilitation training for patients with hand motor impairments. However, the uniqueness of patients' muscle loss is often overlooked. Leveraging reinforcement learning and a…

In contact-rich tasks, like dexterous manipulation, the hybrid nature of making and breaking contact creates challenges for model representation and control. For example, choosing and sequencing contact locations for in-hand manipulation,…

Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities, but training control policies that can directly deploy on real hardware remains difficult due to contact-rich physics and imperfect actuation. We…

Teleoperation systems are essential for efficiently collecting diverse and high-quality robot demonstration data, especially for complex, contact-rich tasks. However, current teleoperation platforms typically lack integrated force feedback,…

Dexterous manipulation of objects in virtual environments with our bare hands, by using only a depth sensor and a state-of-the-art 3D hand pose estimator (HPE), is challenging. While virtual environments are ruled by physics, e.g. object…

Direct and natural interaction is essential for intuitive human-robot collaboration, eliminating the need for additional devices such as joysticks, tablets, or wearable sensors. In this paper, we present a lightweight deep learning-based…