# Interactive Human-in-the-loop Coordination of Manipulation Skills Learned from Demonstration

arXiv: https://arxiv.org/abs/2203.00210 · HTML: https://mlsanity.com/paper/2203.00210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 1 Mar 2022.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Abstract (arXiv)

Learning from demonstration (LfD) provides a fast, intuitive and efficient framework to program robot skills, which has gained growing interest both in research and industrial applications. Most complex manipulation tasks are long-term and involve a set of skill primitives. Thus it is crucial to have a reliable coordination scheme that selects the correct sequence of skill primitive and the correct parameters for each skill, under various scenarios. Instead of relying on a precise simulator, this work proposes a human-in-the-loop coordination framework for LfD skills that: builds parameterized skill models from kinesthetic demonstrations; constructs a geometric task network (GTN) on-the-fly from human instructions; learns a hierarchical control policy incrementally during execution. This framework can reduce significantly the manual design efforts, while improving the adaptability to new scenes. We show on a 7-DoF robotic manipulator that the proposed approach can teach complex industrial tasks such as bin sorting and assembly in less than 30 minutes.