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

Abstract Visual Reinforcement Learning (Visual RL), coupled with high-dimensional
observations, has consistently confronted the long-standing challenge of out-of-distribution …

Abstract This paper introduces Hierarchical Diffusion Policy (HDP) a hierarchical agent for
multi-task robotic manipulation. HDP factorises a manipulation policy into a hierarchical …

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 …

We introduce BiGym, a new benchmark and learning environment for mobile bi-manual
demo-driven robotic manipulation. BiGym features 40 diverse tasks set in home …

Realistic physics engines play a crucial role for learning to manipulate deformable objects
such as garments in simulation. By doing so, researchers can circumvent challenges such …

This paper tackles the challenging robotic task of generalizable paper cutting using scissors.
In this task, scissors attached to a robot arm are driven to accurately cut curves drawn on the …

It is desired to equip robots with the capability of interacting with various soft materials as
they are ubiquitous in the real world. While physics simulations are one of the predominant …

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

Recently, Meta-Auto-Decoder (MAD) was proposed as a novel reduced order model (ROM)
for solving parametric partial differential equations (PDEs), and the best possible …