Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning

no code implementations • 6 Apr 2022 • Eugene Valassakis, Georgios Papagiannis, Norman Di Palo, Edward Johns

We present DOME, a novel method for one-shot imitation learning, where a task can be learned from just a single demonstration and then be deployed immediately, without any further data collection or training.

Imitation Learning Object +1

Learning Eye-in-Hand Camera Calibration from a Single Image

no code implementations • 1 Nov 2021 • Eugene Valassakis, Kamil Dreczkowski, Edward Johns

Eye-in-hand camera calibration is a fundamental and long-studied problem in robotics.

Camera Calibration Pose Estimation +1

Coarse-to-Fine for Sim-to-Real: Sub-Millimetre Precision Across Wide Task Spaces

no code implementations • 24 May 2021 • Eugene Valassakis, Norman Di Palo, Edward Johns

In this paper, we study the problem of zero-shot sim-to-real when the task requires both highly precise control with sub-millimetre error tolerance, and wide task space generalisation.

Motion Planning Pose Estimation

Crossing The Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamics

no code implementations • 15 Aug 2020 • Eugene Valassakis, Zihan Ding, Edward Johns

Zero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and unsolved problem.