# Neurosymbolic Imitation Learning with Human Guidance: A Privileged Information Approach

arXiv: https://arxiv.org/abs/2605.07166 · HTML: https://mlsanity.com/paper/2605.07166/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 8 May 2026.

## Placed in

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

## Builds on (most cited first, 19 of 19)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Unifying distillation and privileged information](https://mlsanity.com/paper/1511.03643/) (2015)
- [Learning Explanatory Rules from Noisy Data](https://mlsanity.com/paper/1711.04574/) (2017)
- [Predicate Invention for Bilevel Planning](https://mlsanity.com/paper/2203.09634/) (2022)
- [AGIL: Learning Attention from Human for Visuomotor Tasks](https://mlsanity.com/paper/1806.03960/) (2018)
- [Learning with privileged information via adversarial discriminative modality distillation](https://mlsanity.com/paper/1810.08437/) (2018)
- [Deep Learning under Privileged Information Using Heteroscedastic Dropout](https://mlsanity.com/paper/1805.11614/) (2018)
- [Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention](https://mlsanity.com/paper/1903.09950/) (2019)
- [Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation](https://mlsanity.com/paper/2102.01295/) (2021)
- [Neuro-Symbolic Forward Reasoning](https://mlsanity.com/paper/2110.09383/) (2021)
- [Toward Understanding Privileged Features Distillation in Learning-to-Rank](https://mlsanity.com/paper/2209.08754/) (2022)
- [Transfer and Marginalize: Explaining Away Label Noise with Privileged Information](https://mlsanity.com/paper/2202.09244/) (2022)
- [ViSaRL: Visual Reinforcement Learning Guided by Human Saliency](https://mlsanity.com/paper/2403.10940/) (2024)
- [Gaze Training by Modulated Dropout Improves Imitation Learning](https://mlsanity.com/paper/1904.08377/) (2019)
- [Mind the Nuisance: Gaussian Process Classification using Privileged Noise](https://mlsanity.com/paper/1407.0179/) (2014)
- [Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents](https://mlsanity.com/paper/2201.02569/) (2022)
- [Imitation Learning with Human Eye Gaze via Multi-Objective Prediction](https://mlsanity.com/paper/2102.13008/) (2021)
- [GABRIL: Gaze-Based Regularization for Mitigating Causal Confusion in Imitation Learning](https://mlsanity.com/paper/2507.19647/) (2025)

## Abstract (arXiv)

Imitation learning is widely used for learning to act in complex environments. While pure neural-based methods handle high dimensional data effectively, they suffer from the requirement of large number of samples and are prone to overfitting. Pure symbolic approaches, while generalize well, do not handle high-dimensional data effectively. We propose a neurosymbolic approach that achieves the best of both worlds, i.e, handling high-dimensional data while achieving generalization. The key advantage of our approach is that it can effectively exploit additional privileged information that is available only during training (in our case, gaze data). Our empirical evaluations demonstrate the effectiveness, efficiency and the generalization capability of our proposed approach.