Robotic grasping is still challenging due to limitations in perception and control, especially
when the CAD models of objects are unknown. Although some grasp planning approaches …

This paper presents the Robot-at-Home dataset (Robot@ Home), a collection of raw and
processed sensory data from domestic settings aimed at serving as a benchmark for …

Humans can instinctively predict whether a given grasp will be successful through visual
and rich haptic feedback. Towards the next generation of smart robotic manufacturing …

We present a learning-based method for representing grasp poses of a high-DOF hand
using neural networks. Due to redundancy in such high-DOF grippers, there exists a large …

What if robots and automation systems were not limited by onboard computation, memory, or
programming? This is now practical with wireless networking and rapidly expanding Internet …

Generating 3D models lies at the core of computer graphics and has been the focus of
decades of research. With the emergence of advanced neural representations and …

We address the problem of estimating the alignment pose between two models using
structure-specific local descriptors. Our descriptors are generated using a combination of 2D …

Transparent objects are encountered frequently in our daily lives, yet recognizing them
poses challenges for conventional vision sensors due to their unique material properties, not …

Intelligent robotic manipulation is a challenging study of machine intelligence. Although
many dexterous robotic hands have been designed to assist or replace human hands in …

The progress in the development of anthropomorphic hands for robotic and prosthetic
applications has not been followed by a parallel development of objective methods to …