# Pedagogical Demonstrations and Pragmatic Learning in Artificial Tutor-Learner Interactions

arXiv: https://arxiv.org/abs/2203.00111 · HTML: https://mlsanity.com/paper/2203.00111/

## Key facts

- Cited by 1 papers in the map (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 2 of its 10 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## 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, 2 of 2)

- [Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration](https://mlsanity.com/paper/2002.09253/) (2020)
- [Help Me Explore: Minimal Social Interventions for Graph-Based Autotelic Agents](https://mlsanity.com/paper/2202.05129/) (2022)

## Built on by (most cited first, 1 of 1)

- [Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2209.12758/) (2022)

## Abstract (arXiv)

When demonstrating a task, human tutors pedagogically modify their behavior by either "showing" the task rather than just "doing" it (exaggerating on relevant parts of the demonstration) or by giving demonstrations that best disambiguate the communicated goal. Analogously, human learners pragmatically infer the communicative intent of the tutor: they interpret what the tutor is trying to teach them and deduce relevant information for learning. Without such mechanisms, traditional Learning from Demonstration (LfD) algorithms will consider such demonstrations as sub-optimal. In this paper, we investigate the implementation of such mechanisms in a tutor-learner setup where both participants are artificial agents in an environment with multiple goals. Using pedagogy from the tutor and pragmatism from the learner, we show substantial improvements over standard learning from demonstrations.