# LSTM-Based Goal Recognition in Latent Space

arXiv: https://arxiv.org/abs/1808.05249 · HTML: https://mlsanity.com/paper/1808.05249/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2018.

## Built on by (most cited first, 8 of 8)

- [Classical Planning in Deep Latent Space](https://mlsanity.com/paper/2107.00110/) (2021)
- [Goal Recognition as a Deep Learning Task: the GRNet Approach](https://mlsanity.com/paper/2210.02377/) (2022)
- [Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue](https://mlsanity.com/paper/2305.11271/) (2023)
- [Fact Probability Vector Based Goal Recognition](https://mlsanity.com/paper/2408.14224/) (2024)
- [Investigating the Combination of Planning-Based and Data-Driven Methods for Goal Recognition](https://mlsanity.com/paper/2301.05608/) (2023)
- [Leveraging Planning Landmarks for Hybrid Online Goal Recognition](https://mlsanity.com/paper/2301.10571/) (2023)
- [Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?](https://mlsanity.com/paper/2306.15362/) (2023)
- [Robots That Take Initiative: A Framework for Building and Evaluating Proactive Robots](https://mlsanity.com/paper/2609.28910/) (2026)

## Abstract (arXiv)

Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals. However, to recognize goals in raw data, recent approaches require either human engineered domain knowledge, or samples of behavior that account for almost all actions being observed to infer possible goals. This is clearly too strong a requirement for real-world applications of goal recognition, and we develop an approach that leverages advances in recurrent neural networks to perform goal recognition as a classification task, using encoded plan traces for training. We empirically evaluate our approach against the state-of-the-art in goal recognition with image-based domains, and discuss under which conditions our approach is superior to previous ones.