29 citations · 66 across the 6 of their papers we have counts for
6 papers
Dynamic Manipulation of Deformable Objects using Imitation Learning with Adaptation to Hardware Constraints
Eric Hannus, Tran Nguyen Le, David Blanco-Mulero +1
Imitation Learning (IL) is a promising paradigm for learning dynamic manipulation of deformable objects since it does not depend on difficult-to-create accurate simulations of such…
Benchmarking the Sim-to-Real Gap in Cloth Manipulation
David Blanco-Mulero, Oriol Barbany, Gokhan Alcan +3
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…
QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation
David Blanco-Mulero, Gokhan Alcan, Fares J. Abu-Dakka +1
Pre-defined manipulation primitives are widely used for cloth manipulation. However, cloth properties such as its stiffness or density can highly impact the performance of these pr…
Co-Imitation: Learning Design and Behaviour by Imitation
Chang Rajani, Karol Arndt, David Blanco-Mulero +2
The co-adaptation of robots has been a long-standing research endeavour with the goal of adapting both body and behaviour of a system for a given task, inspired by the natural evol…
Manipulation of Granular Materials by Learning Particle Interactions
Neea Tuomainen, David Blanco-Mulero, Ville Kyrki
Manipulation of granular materials such as sand or rice remains an unsolved problem due to challenges such as the difficulty of defining their configuration or modeling the materia…
Evolving-Graph Gaussian Processes
David Blanco-Mulero, Markus Heinonen, Ville Kyrki
Graph Gaussian Processes (GGPs) provide a data-efficient solution on graph structured domains. Existing approaches have focused on static structures, whereas many real graph data r…