相关论文: Principal Components of Touch

Tactile sensing is critical to fine-grained, contact-rich manipulation tasks, such as insertion and assembly. Prior research has shown the possibility of learning tactile-guided policy from teleoperated demonstration data. However, to…

Tactile perception is important for robotic systems that interact with the world through touch. Touch is an active sense in which tactile measurements depend on the contact properties of an interaction--e.g., velocity, force,…

Robots can better interact with humans and unstructured environments through touch sensing. However, most commercial robots are not equipped with tactile skins, making it challenging to achieve even basic touch-sensing functions, such as…

Humans perceive the world by interacting with objects, which often happens in a dynamic way. For example, a human would shake a bottle to guess its content. However, it remains a challenge for robots to understand many dynamic signals…

To use robots in more unstructured environments, we have to accommodate for more complexities. Robotic systems need more awareness of the environment to adapt to uncertainty and variability. Although cameras have been predominantly used in…

Camera-based tactile sensors provide robots with a high-performance tactile sensing approach for environment perception and dexterous manipulation. However, achieving comprehensive environmental perception still requires cooperation with…

Our research investigates vibrotactile perception in four prosthetic hands with distinct kinematics and mechanical characteristics. We found that rigid and simple socket-based prosthetic devices can transmit tactile information and…

We address the problem of tracking 3D object poses from touch during in-hand manipulations. Specifically, we look at tracking small objects using vision-based tactile sensors that provide high-dimensional tactile image measurements at the…

Due to the complexity of modeling the elastic properties of materials, the use of machine learning algorithms is continuously increasing for tactile sensing applications. Recent advances in deep neural networks applied to computer vision…

This paper proposes a novel framework for utilizing skin sensors as a new operation interface of complex robots. The skin sensors employed in this study possess the capability to quantify multimodal tactile information at multiple contact…

Cellular Automata are discrete dynamical systems that evolve following simple and local rules. Despite of its local simplicity, knowledge discovery in CA is a NP problem. This is the main motivation for using data mining techniques for CA…

Principal component analysis (PCA) is by far the most widespread tool for unsupervised learning with high-dimensional data sets. Its application is popularly studied for the purpose of exploratory data analysis and online process…

Whole body tactile perception via tactile skins offers large benefits for robots in unstructured environments. To fully realize this benefit, tactile systems must support real-time data acquisition over a massive number of tactile sensor…

Principal Component Analysis (PCA) is a fundamental data preprocessing tool in the world of machine learning. While PCA is often thought of as a dimensionality reduction method, the purpose of PCA is actually two-fold: dimension reduction…

For machines to interact with the physical world, they must understand the physical properties of objects and materials they encounter. We use fabrics as an example of a deformable material with a rich set of mechanical properties. A thin…

The tactile properties of tissue, such as elasticity and stiffness, often play an important role in surgical oncology when identifying tumors and pathological tissue boundaries. Though extremely valuable, robot-assisted surgery comes at the…

Across a plethora of social situations, we touch others in natural and intuitive ways to share thoughts and emotions, such as tapping to get one's attention or caressing to soothe one's anxiety. A deeper understanding of these…

The literature provides strong evidence that stock prices can be predicted from past price data. Principal component analysis (PCA) is a widely used mathematical technique for dimensionality reduction and analysis of data by identifying a…

Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA…

The importance of force perception in interacting with the environment was proven years ago. However, it is still a challenge to measure the contact force distribution accurately in real-time. In order to break through this predicament, we…