In this paper we introduce SMPLicit, a novel generative model to jointly represent body
pose, shape and clothing geometry. In contrast to existing learning-based approaches that …

Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a
long-standing challenge in robotic manipulation. The complex dynamics and high …

The rise of deep learning has brought remarkable progress in estimating hand geometry
from images where the hands are part of the scene. This paper focuses on a new problem …

With the rapid development of machine learning, its powerful function in the machine vision
field is increasingly reflected. The combination of machine vision and robotics to achieve the …

Abstract Deep Reinforcement Learning (DRL), which can learn complex policies with high-
dimensional observations as inputs, eg, images, has been successfully applied to various …

Sequential pulling policies to flatten and smooth fabrics have applications from surgery to
manufacturing to home tasks such as bed making and folding clothes. Due to the complexity …

In the growing field of study known as “soft robotics,“highly adaptable robots are built for soft
interactions by utilizing the acquiescence, and flexibility of soft structures. The substantial …

In this paper we present a Deep Reinforcement Learning approach to solve dynamic cloth
manipulation tasks. Differing from the case of rigid objects, we stress that the followed …

The realm of textiles spans clothing, households, healthcare, sports, and industrial
applications. The deformable nature of these objects poses unique challenges that prior …

Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key
regions, such as corners, are not directly graspable, making manipulation difficult. We …