132 citations · 173 across the 13 of their papers we have counts for
12 papers · 1 filter
Real-time Mapping of Physical Scene Properties with an Autonomous Robot Experimenter
Iain Haughton, Edgar Sucar, Andre Mouton +2
Neural fields can be trained from scratch to represent the shape and appearance of 3D scenes efficiently. It has also been shown that they can densely map correlated properties suc…
My House, My Rules: Learning Tidying Preferences with Graph Neural Networks
Ivan Kapelyukh, Edward Johns
Robots that arrange household objects should do so according to the user's preferences, which are inherently subjective and difficult to model. We present NeatNet: a novel Variatio…
Learning Eye-in-Hand Camera Calibration from a Single Image
Eugene Valassakis, Kamil Dreczkowski, Edward Johns
Eye-in-hand camera calibration is a fundamental and long-studied problem in robotics. We present a study on using learning-based methods for solving this problem online from a sing…
Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration
Edward Johns
We introduce a simple new method for visual imitation learning, which allows a novel robot manipulation task to be learned from a single human demonstration, without requiring any…
Coarse-to-Fine for Sim-to-Real: Sub-Millimetre Precision Across Wide Task Spaces
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 generalisat…
DROID: Minimizing the Reality Gap using Single-Shot Human Demonstration
Ya-Yen Tsai, Hui Xu, Zihan Ding +3
Reinforcement learning (RL) has demonstrated great success in the past several years. However, most of the scenarios focus on simulated environments. One of the main challenges of…