# Planning with Learned Subgoals Selected by Temporal Information

arXiv: https://arxiv.org/abs/2410.20272 · HTML: https://mlsanity.com/paper/2410.20272/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2024.

## Built on by (most cited first, 2 of 2)

- [Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities](https://mlsanity.com/paper/2603.24318/) (2026)
- [MoRe-ERL: Learning Motion Residuals using Episodic Reinforcement Learning](https://mlsanity.com/paper/2508.01409/) (2025)

## Abstract (arXiv)

Path planning in a changing environment is a challenging task in robotics, as moving objects impose time-dependent constraints. Recent planning methods primarily focus on the spatial aspects, lacking the capability to directly incorporate time constraints. In this paper, we propose a method that leverages a generative model to decompose a complex planning problem into small manageable ones by incrementally generating subgoals given the current planning context. Then, we take into account the temporal information and use learned time estimators based on different statistic distributions to examine and select the generated subgoal candidates. Experiments show that planning from the current robot state to the selected subgoal can satisfy the given time-dependent constraints while being goal-oriented.