# M4Diffuser: Multi-View Diffusion Policy with Manipulability-Aware Control for Robust Mobile Manipulation

arXiv: https://arxiv.org/abs/2509.14980 · HTML: https://mlsanity.com/paper/2509.14980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 35 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 24 of 24)

- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [ALOHA Unleashed: A Simple Recipe for Robot Dexterity](https://mlsanity.com/paper/2410.13126/) (2024)
- [The Ingredients for Robotic Diffusion Transformers](https://mlsanity.com/paper/2410.10088/) (2024)
- [Transformer-based deep imitation learning for dual-arm robot manipulation](https://mlsanity.com/paper/2108.00385/) (2021)
- [M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.11402/) (2024)
- [AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation](https://mlsanity.com/paper/2507.01961/) (2025)
- [A Holistic Approach to Reactive Mobile Manipulation](https://mlsanity.com/paper/2109.04749/) (2021)
- [Demonstrating Mobile Manipulation in the Wild: A Metrics-Driven Approach](https://mlsanity.com/paper/2401.01474/) (2024)
- [ContactDexNet: Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping](https://mlsanity.com/paper/2404.08844/) (2024)
- [Active-Perceptive Motion Generation for Mobile Manipulation](https://mlsanity.com/paper/2310.00433/) (2023)
- [Don't Let Your Robot be Harmful: Responsible Robotic Manipulation via Safety-as-Policy](https://mlsanity.com/paper/2411.18289/) (2024)
- [HoMeR: Learning In-the-Wild Mobile Manipulation via Hybrid Imitation and Whole-Body Control](https://mlsanity.com/paper/2506.01185/) (2025)
- [Towards Fusing Point Cloud and Visual Representations for Imitation Learning](https://mlsanity.com/paper/2502.12320/) (2025)
- [ToolEENet: Tool Affordance 6D Pose Estimation](https://mlsanity.com/paper/2404.04193/) (2024)
- [An Architecture for Reactive Mobile Manipulation On-The-Move](https://mlsanity.com/paper/2212.06991/) (2022)
- [Reactive Base Control for On-The-Move Mobile Manipulation in Dynamic Environments](https://mlsanity.com/paper/2309.09393/) (2023)
- [Motion Planning for Robotics: A Review for Sampling-based Planners](https://mlsanity.com/paper/2410.19414/) (2024)
- [Exploiting Local Observations for Robust Robot Learning](https://mlsanity.com/paper/2309.14792/) (2023)
- [RMMI: Reactive Mobile Manipulation using an Implicit Neural Map](https://mlsanity.com/paper/2408.16206/) (2024)
- [EHC-MM: Embodied Holistic Control for Mobile Manipulation](https://mlsanity.com/paper/2409.08527/) (2024)
- [Tree-Based Grafting Approach for Bidirectional Motion Planning with Local Subsets Optimization](https://mlsanity.com/paper/2508.19776/) (2025)
- [Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler](https://mlsanity.com/paper/2508.21549/) (2025)

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

- [From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation](https://mlsanity.com/paper/2603.09415/) (2026)
- [FALCON: Actively Decoupled Visuomotor Policies for Loco-Manipulation with Foundation-Model-Based Coordination](https://mlsanity.com/paper/2512.04381/) (2025)

## Abstract (arXiv)

Mobile manipulation requires the coordinated control of a mobile base and a robotic arm while simultaneously perceiving both global scene context and fine-grained object details. Existing single-view approaches often fail in unstructured environments due to limited fields of view, exploration, and generalization abilities. Moreover, classical controllers, although stable, struggle with efficiency and manipulability near singularities. To address these challenges, we propose M4Diffuser, a hybrid framework that integrates a Multi-View Diffusion Policy with a novel Reduced and Manipulability-aware QP (ReM-QP) controller for mobile manipulation. The diffusion policy leverages proprioceptive states and complementary camera perspectives with both close-range object details and global scene context to generate task-relevant end-effector goals in the world frame. These high-level goals are then executed by the ReM-QP controller, which eliminates slack variables for computational efficiency and incorporates manipulability-aware preferences for robustness near singularities. Comprehensive experiments in simulation and real-world environments show that M4Diffuser achieves 7 to 56 percent higher success rates and reduces collisions by 3 to 31 percent over baselines. Our approach demonstrates robust performance for smooth whole-body coordination, and strong generalization to unseen tasks, paving the way for reliable mobile manipulation in unstructured environments. Details of the demo and supplemental material are available on our project website https://sites.google.com/view/m4diffuser.