# HMPO: Human Motion Prediction in Occluded Environments for Safe Motion Planning

arXiv: https://arxiv.org/abs/2006.00424 · HTML: https://mlsanity.com/paper/2006.00424/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 3 of 3)

- [Multi-Agent Variational Occlusion Inference Using People as Sensors](https://mlsanity.com/paper/2109.02173/) (2021)
- [Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models](https://mlsanity.com/paper/2210.14584/) (2022)
- [EquiFusion: Kinematics-Agnostic Human Motion Prediction via Equivariant Latent Diffusion](https://mlsanity.com/paper/2607.10984/) (2026)

## Abstract (arXiv)

We present a novel approach to generate collision-free trajectories for a robot operating in close proximity with a human obstacle in an occluded environment. The self-occlusions of the robot can significantly reduce the accuracy of human motion prediction, and we present a novel deep learning-based prediction algorithm. Our formulation uses CNNs and LSTMs and we augment human-action datasets with synthetically generated occlusion information for training. We also present an occlusion-aware planner that uses our motion prediction algorithm to compute collision-free trajectories. We highlight performance of the overall approach (HMPO) in complex scenarios and observe upto 68% performance improvement in motion prediction accuracy, and 38% improvement in terms of error distance between the ground-truth and the predicted human joint positions.