# Incorporating Human Domain Knowledge into Large Scale Cost Function Learning

arXiv: https://arxiv.org/abs/1612.04318 · HTML: https://mlsanity.com/paper/1612.04318/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2016.

## Built on by (most cited first, 5 of 5)

- [Learning a Prior over Intent via Meta-Inverse Reinforcement Learning](https://mlsanity.com/paper/1805.12573/) (2018)
- [Massively Scalable Inverse Reinforcement Learning in Google Maps](https://mlsanity.com/paper/2305.11290/) (2023)
- [PACER: Preference-conditioned All-terrain Costmap Generation](https://mlsanity.com/paper/2410.23488/) (2024)
- [Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning](https://mlsanity.com/paper/1806.08479/) (2018)
- [Pixel State Value Network for Combined Prediction and Planning in Interactive Environments](https://mlsanity.com/paper/2310.07706/) (2023)

## Abstract (arXiv)

Recent advances have shown the capability of Fully Convolutional Neural Networks (FCN) to model cost functions for motion planning in the context of learning driving preferences purely based on demonstration data from human drivers. While pure learning from demonstrations in the framework of Inverse Reinforcement Learning (IRL) is a promising approach, we can benefit from well informed human priors and incorporate them into the learning process. Our work achieves this by pretraining a model to regress to a manual cost function and refining it based on Maximum Entropy Deep Inverse Reinforcement Learning. When injecting prior knowledge as pretraining for the network, we achieve higher robustness, more visually distinct obstacle boundaries, and the ability to capture instances of obstacles that elude models that purely learn from demonstration data. Furthermore, by exploiting these human priors, the resulting model can more accurately handle corner cases that are scarcely seen in the demonstration data, such as stairs, slopes, and underpasses.