# Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks

arXiv: https://arxiv.org/abs/2104.08137 · HTML: https://mlsanity.com/paper/2104.08137/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2021.

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

- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [HOIMotion: Forecasting Human Motion During Human-Object Interactions Using Egocentric 3D Object Bounding Boxes](https://mlsanity.com/paper/2407.02633/) (2024)
- [GazeMotion: Gaze-guided Human Motion Forecasting](https://mlsanity.com/paper/2403.09885/) (2024)
- [Visualizing the Diversity of Representations Learned by Bayesian Neural Networks](https://mlsanity.com/paper/2201.10859/) (2022)
- [A Task-Efficient Reinforcement Learning Task-Motion Planner for Safe Human-Robot Cooperation](https://mlsanity.com/paper/2510.12477/) (2025)

## Abstract (arXiv)

In this paper, we tackle the problem of human-robot coordination in sequences of manipulation tasks. Our approach integrates hierarchical human motion prediction with Task and Motion Planning (TAMP). We first devise a hierarchical motion prediction approach by combining Inverse Reinforcement Learning and short-term motion prediction using a Recurrent Neural Network. In a second step, we propose a dynamic version of the TAMP algorithm Logic-Geometric Programming (LGP). Our version of Dynamic LGP, replans periodically to handle the mismatch between the human motion prediction and the actual human behavior. We assess the efficacy of the approach by training the prediction algorithms and testing the framework on the publicly available MoGaze dataset.