# Improving Robot-Centric Learning from Demonstration via Personalized Embeddings

arXiv: https://arxiv.org/abs/2110.03134 · HTML: https://mlsanity.com/paper/2110.03134/

## Key facts

- Cited by 2 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 8 of its 29 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## 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, 8 of 8)

- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://mlsanity.com/paper/1810.02890/) (2018)
- [EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning](https://mlsanity.com/paper/1807.08364/) (2018)
- [Interactive Policy Learning through Confidence-Based Autonomy](https://mlsanity.com/paper/1401.3439/) (2014)
- [Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations](https://mlsanity.com/paper/1610.00850/) (2016)
- [Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations](https://mlsanity.com/paper/1906.06397/) (2019)
- [Four Years in Review: Statistical Practices of Likert Scales in Human-Robot Interaction Studies](https://mlsanity.com/paper/2001.03231/) (2020)
- [Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration](https://mlsanity.com/paper/1903.06047/) (2019)

## Built on by (most cited first, 2 of 2)

- [MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving](https://mlsanity.com/paper/2301.08595/) (2023)
- [Do as the Romans Do: Learning Universal Behaviors from Heterogeneous Agents](https://mlsanity.com/paper/2606.18537/) (2026)

## Abstract (arXiv)

Learning from demonstration (LfD) techniques seek to enable novice users to teach robots novel tasks in the real world. However, prior work has shown that robot-centric LfD approaches, such as Dataset Aggregation (DAgger), do not perform well with human teachers. DAgger requires a human demonstrator to provide corrective feedback to the learner either in real-time, which can result in degraded performance due to suboptimal human labels, or in a post hoc manner which is time intensive and often not feasible. To address this problem, we present Mutual Information-driven Meta-learning from Demonstration (MIND MELD), which meta-learns a mapping from poor quality human labels to predicted ground truth labels, thereby improving upon the performance of prior LfD approaches for DAgger-based training. The key to our approach for improving upon suboptimal feedback is mutual information maximization via variational inference. Our approach learns a meaningful, personalized embedding via variational inference which informs the mapping from human provided labels to predicted ground truth labels. We demonstrate our framework in a synthetic domain and in a human-subjects experiment, illustrating that our approach improves upon the corrective labels provided by a human demonstrator by 63%.