# DeepMoTIon: Learning to Navigate Like Humans

arXiv: https://arxiv.org/abs/1803.03719 · HTML: https://mlsanity.com/paper/1803.03719/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation](https://mlsanity.com/paper/2101.02647/) (2021)
- [Fairness and Bias in Robot Learning](https://mlsanity.com/paper/2207.03444/) (2022)
- [DeepSocNav: Social Navigation by Imitating Human Behaviors](https://mlsanity.com/paper/2107.09170/) (2021)
- [Ratatouille: Imitation Learning Ingredients for Real-world Social Robot Navigation](https://mlsanity.com/paper/2509.17204/) (2025)
- [Dense Crowd Flow-Informed Path Planning](https://mlsanity.com/paper/2206.00705/) (2022)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)

## Abstract (arXiv)

We present a novel human-aware navigation approach, where the robot learns to mimic humans to navigate safely in crowds. The presented model, referred to as DeepMoTIon, is trained with pedestrian surveillance data to predict human velocity in the environment. The robot processes LiDAR scans via the trained network to navigate to the target location. We conduct extensive experiments to assess the components of our network and prove their necessity to imitate humans. Our experiments show that DeepMoTIion outperforms all the benchmarks in terms of human imitation, achieving a 24% reduction in time series-based path deviation over the next best approach. In addition, while many other approaches often failed to reach the target, our method reached the target in 100% of the test cases while complying with social norms and ensuring human safety.