Learning Multi-Stage Tasks with One Demonstration via Self-Replay

Norman Di Palo, Edward Johns

Keywords: Imitation Learning, Multi-Stage Imitation Learning, Robot Manipulation

Abstract:

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 recent Coarse-to-Fine Imitation Learning, we model imitation learning as a learned object reaching phase followed by an open-loop replay of the operator's actions. We build upon this for multi-stage tasks where, following the human demonstration, the robot can autonomously collect image data for the entire multi-stage task, by reaching the next object in the sequence and then replaying the demonstration, repeating in a loop for all stages of the task. We evaluate with real-world experiments on a set of everyday multi-stage tasks, which we show that our method can solve from a single demonstration.
Videos and supplementary material can be found at this webpage: https://www.robot-learning.uk/self-replay.