Rugile Pevceviciute — accepted AI-conference papers

2025

DemoStart: Demonstration-Led Auto-Curriculum Applied to Sim-to-Real with Multi-Fingered Robots

Maria Bauzá, Jose Enriaue Chen, Valentin Dalibard, Nimrod Gileadi, Roland Hafner, Murilo F. Martins +10

ICRA 2025

We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a three- fingered robotic hand, from only a sparse reward and a handful of demonstrations in simulation. Learning from simulation drastically reduces

Cited by 12 Source ↗Scholar ↗

2022

Offline Meta-Reinforcement Learning for Industrial Insertion

Tony Z. Zhao, Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Nicolas Heess, Jon Scholz +2

ICRA 2022poster

Reinforcement learning (RL) can in principle let robots automatically adapt to new tasks, but current RL methods require a large number of trials to accomplish this. In this paper, we tackle rapid adaptation to new tasks through the framework of meta-learning, which utilizes past tasks to learn to a…

Cited by 104 Source ↗Scholar ↗

2020

S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency

Mel Vecerik, Jean-Baptiste Regli, Oleg Sushkov, David Barker, Rugile Pevceviciute, Thomas Rothörl +3

CoRL 2020

A robot’s ability to act is fundamentally constrained by what it can perceive. Many existing approaches to visual representation learning utilize general-purpose training criteria, e.g. image reconstruction, smoothness in latent space, or usefulness for control, or else make use of large datasets an

Cited by 0 Source ↗PDF ↗Scholar ↗