# History Encoding Representation Design for Human Intention Inference

arXiv: https://arxiv.org/abs/2106.02222 · HTML: https://mlsanity.com/paper/2106.02222/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer](https://mlsanity.com/paper/2011.03148/) (2020)
- [End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning](https://mlsanity.com/paper/2003.12464/) (2020)
- [Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control](https://mlsanity.com/paper/1812.03216/) (2018)
- [Guided Policy Search Model-based Reinforcement Learning for Urban Autonomous Driving](https://mlsanity.com/paper/2005.03076/) (2020)
- [Cascade Attribute Network: Decomposing Reinforcement Learning Control Policies using Hierarchical Neural Networks](https://mlsanity.com/paper/2005.04213/) (2020)
- [COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing](https://mlsanity.com/paper/2011.11270/) (2020)
- [Cascade Attribute Learning Network](https://mlsanity.com/paper/1711.09142/) (2017)

## Built on by (most cited first, 1 of 1)

- [MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts](https://mlsanity.com/paper/2403.06041/) (2024)

## Abstract (arXiv)

In this extended abstract, we investigate the design of learning representation for human intention inference. In our designed human intention prediction task, we propose a history encoding representation that is both interpretable and effective for prediction. Through extensive experiments, we show our prediction framework with a history encoding representation design is successful on the human intention prediction problem.