# Efficient Supervision for Robot Learning via Imitation, Simulation, and Adaptation

arXiv: https://arxiv.org/abs/1904.07346 · HTML: https://mlsanity.com/paper/1904.07346/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Learning Norms from Stories: A Prior for Value Aligned Agents](https://mlsanity.com/paper/1912.03553/) (2019)
- [Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior](https://mlsanity.com/paper/2104.09469/) (2021)
- [Machine Learning Approaches for Principle Prediction in Naturally Occurring Stories](https://mlsanity.com/paper/2212.06048/) (2022)
- [The Goofus & Gallant Story Corpus for Practical Value Alignment](https://mlsanity.com/paper/2501.09707/) (2025)

## Abstract (arXiv)

Recent successes in machine learning have led to a shift in the design of autonomous systems, improving performance on existing tasks and rendering new applications possible. Data-focused approaches gain relevance across diverse, intricate applications when developing data collection and curation pipelines becomes more effective than manual behaviour design. The following work aims at increasing the efficiency of this pipeline in two principal ways: by utilising more powerful sources of informative data and by extracting additional information from existing data. In particular, we target three orthogonal fronts: imitation learning, domain adaptation, and transfer from simulation.