154 citations · 765 across the 31 of their papers we have counts for
9 papers · 1 filter
Back to Reality for Imitation Learning
Edward Johns
Imitation learning, and robot learning in general, emerged due to breakthroughs in machine learning, rather than breakthroughs in robotics. As such, evaluation metrics for robot le…
Learning Multi-Stage Tasks with One Demonstration via Self-Replay
Norman Di Palo, Edward Johns
In this work, we introduce a novel method to learn everyday-like multi-stage tasks from a single human demonstration, without requiring any prior object knowledge. Inspired by the…
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…
Hybrid ICP
Kamil Dreczkowski, Edward Johns
ICP algorithms typically involve a fixed choice of data association method and a fixed choice of error metric. In this paper, we propose Hybrid ICP, a novel and flexible ICP varian…
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…