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

In recent years, the increasing complexity and safety-critical nature of robotic tasks have
highlighted the importance of accurate and reliable robot models. This trend has led to a …

Abstract Learning from Demonstration (LfD) is an efficient technique for robots to acquire
new skills through expert observation, significantly mitigating the need for laborious manual …

Various pose estimation and tracking problems in robotics can be decomposed into a
correspondence estimation problem (often computed using a deep network) followed by a …

In this work, we aim to learn dexterous manipulation of deformable objects using multi-
fingered hands. Reinforcement learning approaches for dexterous rigid object manipulation …

Mastering dexterous robotic manipulation of deformable objects is vital for overcoming the
limitations of parallel grippers in real-world applications. Current trajectory optimisation …

Differentiable simulators continue to push the state of the art across a range of domains
including computational physics, robotics, and machine learning. Their main value is the …

Robotic manipulation can greatly benefit from the data efficiency, robustness, and
predictability of model-based methods if robots can quickly generate models of novel objects …

The emergence of differentiable simulators enabling analytic gradient computation has
motivated a new wave of learning algorithms that hold the potential to significantly increase …