相关论文: Rapid manufacturing of color-based hemispherical s…
The study of dexterous manipulation has provided important insights in humans sensorimotor control as well as inspiration for manipulation strategies in robotic hands. Previous work focused on experimental environment with restrictions.…
We present a tactile sensing method enabled by the mechanical compliance of soft robots; an externally attachable photoreflective module reads surface deformation of silicone skin to estimate contact force without embedding tactile…
Tactile sensing represents a crucial technique that can enhance the performance of robotic manipulators in various tasks. This work presents a novel bioinspired neuromorphic vision-based tactile sensor that uses an event-based camera to…
As more robots are implemented for contact-rich tasks, tactile sensors are in increasing demand. For many circumstances, the contact is required to be compliant, and soft sensors are in need. This paper introduces a novelly designed soft…
In conjunction with huge recent progress in camera and computer vision technology, camera-based sensors have increasingly shown considerable promise in relation to tactile sensing. In comparison to competing technologies (be they resistive,…
Contact often occurs without macroscopic surface deformation, such as during interaction with liquids, semi-liquids, or ultra-soft materials. However, most existing tactile sensors rely on deformation to infer contact, making such…
Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility…
Soft robotics is an emerging field that yields promising results for tasks that require safe and robust interactions with the environment or with humans, such as grasping, manipulation, and human-robot interaction. Soft robots rely on…
We propose a hardware and software pipeline to fabricate flexible wearable sensors and use them to capture deformations without line of sight. Our first contribution is a low-cost fabrication pipeline to embed multiple aligned conductive…
Deep learning and reinforcement learning methods have been shown to enable learning of flexible and complex robot controllers. However, the reliance on large amounts of training data often requires data collection to be carried out in…
Grasping object,whether they are flat, round, or narrow and whether they have regular or irregular shapes,introduces difficulties in determining the ideal grasping posture, even for the most state-of-the-art grippers. In this article, we…
Manipulating fragile objects in environments such as homes and factories requires stable and gentle grasping along with precise and safe placement. Compared to traditional rigid grippers, the use of soft grippers reduces the control…
Conventional industrial robots often use two-fingered grippers or suction cups to manipulate objects or interact with the world. Because of their simplified design, they are unable to reproduce the dexterity of human hands when manipulating…
We introduce a spherical fingertip sensor for dynamic manipulation. It is based on barometric pressure and time-of-flight proximity sensors and is low-latency, compact, and physically robust. The sensor uses a trained neural network to…
Deformable object manipulation is a classical and challenging research area in robotics. Compared with rigid object manipulation, this problem is more complex due to the deformation properties including elastic, plastic, and elastoplastic…
Knowledge of 3-D object shape is of great importance to robot manipulation tasks, but may not be readily available in unstructured environments. While vision is often occluded during robot-object interaction, high-resolution tactile sensors…
Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using touch to perceive and interact with the surrounding…
Despite significant advances in touch and force transduction, tactile sensing is still far from ubiquitous in robotic manipulation. Existing methods for building touch sensors have proven difficult to integrate into robot fingers due to…
While tactile sensor technology has made great strides over the past decades, applications in robotic manipulation are limited by aspects such as blind spots, difficult integration into hands, and low spatial resolution. We present a method…
Traditional methods for achieving high localization accuracy on tactile sensors usually involve a matrix of miniaturized individual sensors distributed on the area of interest. This approach usually comes at a price of increased complexity…