# Subgoal Diffuser: Coarse-to-fine Subgoal Generation to Guide Model Predictive Control for Robot Manipulation

arXiv: https://arxiv.org/abs/2403.13085 · HTML: https://mlsanity.com/paper/2403.13085/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2024.

## Built on by (most cited first, 12 of 12)

- [Diffusion Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2504.08438/) (2025)
- [Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models](https://mlsanity.com/paper/2412.19948/) (2024)
- [Efficient Robotic Policy Learning via Latent Space Backward Planning](https://mlsanity.com/paper/2505.06861/) (2025)
- [CHD: Coupled Hierarchical Diffusion for Long-Horizon Tasks](https://mlsanity.com/paper/2505.07261/) (2025)
- [Cascaded Diffusion Models for Neural Motion Planning](https://mlsanity.com/paper/2505.15157/) (2025)
- [Multimodal Diffusion Forcing for Forceful Manipulation](https://mlsanity.com/paper/2511.04812/) (2025)
- [Diffusion Subgoal Planning for Long-Horizon Offline Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2609.34575/) (2026)
- [Continuous Reasoning for Vision-Language-Action](https://mlsanity.com/paper/2606.00229/) (2026)
- [Implicit Contact Diffuser: Sequential Contact Reasoning with Latent Point Cloud Diffusion](https://mlsanity.com/paper/2410.16571/) (2024)
- [Adaptive Coarse-to-Fine Subgoal Refinement for Long-Horizon Offline Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2605.28127/) (2026)
- [NEEDLEWORK: Offline Rewriting of Robot Data with Verified Local Stitches](https://mlsanity.com/paper/2610.02339/) (2026)
- [Acceptable Misalignment](https://mlsanity.com/paper/2610.16881/) (2026)

## Abstract (arXiv)

Manipulation of articulated and deformable objects can be difficult due to their compliant and under-actuated nature. Unexpected disturbances can cause the object to deviate from a predicted state, making it necessary to use Model-Predictive Control (MPC) methods to plan motion. However, these methods need a short planning horizon to be practical. Thus, MPC is ill-suited for long-horizon manipulation tasks due to local minima. In this paper, we present a diffusion-based method that guides an MPC method to accomplish long-horizon manipulation tasks by dynamically specifying sequences of subgoals for the MPC to follow. Our method, called Subgoal Diffuser, generates subgoals in a coarse-to-fine manner, producing sparse subgoals when the task is easily accomplished by MPC and more dense subgoals when the MPC method needs more guidance. The density of subgoals is determined dynamically based on a learned estimate of reachability, and subgoals are distributed to focus on challenging parts of the task. We evaluate our method on two robot manipulation tasks and find it improves the planning performance of an MPC method, and also outperforms prior diffusion-based methods.