# Enabling Robots to Identify Missing Steps in Robot Tasks for Guided Learning from Demonstration

arXiv: https://arxiv.org/abs/2311.18355 · HTML: https://mlsanity.com/paper/2311.18355/

## 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.
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Placed in

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

## Builds on (most cited first, 6 of 6)

- [Translating Natural Language to Planning Goals with Large-Language Models](https://mlsanity.com/paper/2302.05128/) (2023)
- [Learning Partially Observable Deterministic Action Models](https://mlsanity.com/paper/1401.3437/) (2014)
- [Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models for AI Planning](https://mlsanity.com/paper/2311.13720/) (2023)
- [MACQ: A Holistic View of Model Acquisition Techniques](https://mlsanity.com/paper/2206.06530/) (2022)
- [Explainable Human-Robot Training and Cooperation with Augmented Reality](https://mlsanity.com/paper/2302.01039/) (2023)
- [Virtual, Augmented, and Mixed Reality for Human-Robot Interaction: A Survey and Virtual Design Element Taxonomy](https://mlsanity.com/paper/2202.11249/) (2022)

## Abstract (arXiv)

Learning from Demonstration (LfD) systems are commonly used to teach robots new tasks by generating a set of skills from user-provided demonstrations. These skills can then be sequenced by planning algorithms to execute complex tasks. However, LfD systems typically require a full demonstration of the entire task, even when parts of it are already known to the robot. This limitation comes from the system's inability to recognize which sub-tasks are already familiar, leading to a repetitive and burdensome demonstration process for users. In this paper, we introduce a new method for guided demonstrations that reduces this burden, by helping the robot to identify which parts of the task it already knows, considering the overall task goal and the robot's existing skills. In particular, through a combinatorial search, the method finds the smallest necessary change in the initial task conditions that allows the robot to solve the task with its current knowledge. This state is referred to as the excuse state. The human demonstrator is then only required to teach how to reach the excuse state (missing sub-task), rather than demonstrating the entire task. Empirical results and a pilot user study show that our method reduces demonstration time by 61% and decreases the size of demonstrations by 72%.