# I-Planner: Intention-Aware Motion Planning Using Learning Based Human Motion Prediction

arXiv: https://arxiv.org/abs/1608.04837 · HTML: https://mlsanity.com/paper/1608.04837/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2016.

## Built on by (most cited first, 4 of 4)

- [Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments](https://mlsanity.com/paper/2102.05382/) (2021)
- [Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios](https://mlsanity.com/paper/2107.04538/) (2021)
- [Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction](https://mlsanity.com/paper/2310.03314/) (2023)
- [A Task-Efficient Reinforcement Learning Task-Motion Planner for Safe Human-Robot Cooperation](https://mlsanity.com/paper/2510.12477/) (2025)

## Abstract (arXiv)

We present a motion planning algorithm to compute collision-free and smooth trajectories for high-DOF robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We also describe novel techniques to account for noise in human motion prediction. We highlight the performance of our planning algorithm in complex simulated scenarios and real world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments.