M4Diffuser: Diffusion in Mobile Manipulation

• The paper introduces a novel multi-view diffusion policy that fuses RGB visual inputs and proprioceptive data using a Transformer encoder to generate optimal end-effector goals.
• The paper presents a reduced, manipulability-aware QP controller that eliminates slack variables to improve computational efficiency and motion smoothness.
• Experimental results demonstrate significantly reduced task completion times, higher success rates, and lower collision rates in both simulation and real-world settings.

M4Diffuser encompasses a suite of methodologies and frameworks spanning electromagnetic simulations, deep learning architectures, generative modeling, control theory, and multimodal sensor fusion, unified by the central principle of “diffusion” in either data, signal, or control domains. Recent work on M4Diffuser (Dong et al., 18 Sep 2025) introduces a hybrid architecture for mobile manipulation that integrates multi-view perception, a diffusion-based policy for action generation, and a novel quadratic programming (QP) controller with manipulability awareness. The following sections dissect the technical, mathematical, and experimental components that define M4Diffuser across relevant modeling and application domains.

1. Multi-View Diffusion Policy for Mobile Manipulation

The multi-view diffusion policy in M4Diffuser fuses information from three RGB cameras—offering distinct viewpoints for global scene context and fine-grained object details—with proprioceptive states of the robot. Visual data are encoded via convolutional neural networks, concatenated with proprioceptive features, and processed by a Transformer-based encoder implementing cross-view fusion and temporal self-attention. The result is a latent representation $h$ that jointly captures the spatial and task-relevant characteristics of the unstructured environment.

Upon this fused latent, a denoising diffusion process is implemented to generate end-effector goals. The forward process is defined as

$$q(a_k\,|\,a_0) = \mathcal{N}(a_k; \sqrt{\bar{\alpha}_k} a_0, (1-\bar{\alpha}_k)I)$$

with $\bar{\alpha}_k = \prod_{s=1}^k (1 - \beta_s)$ and $\beta_s$ a noise schedule parameter. The reverse model predicts a sequence of actions ($a_{k-1}$) via

$$p_\theta(a_{k-1}\,|\,a_k, h) = \mathcal{N}(a_{k-1}; \mu_\theta(a_k, k, h), \Sigma_\theta(a_k, k, h))$$

This diffusion process “denoises” random noise into feasible and contextually optimal end-effector poses $T^*_e$ in the world coordinate frame, which are subsequently servoed into velocity commands for execution.

The training objective optimizes the mean squared error on the predicted noise:

$$\mathcal{L}_{\mathrm{diff}} = \mathbb{E}_{a_0, \epsilon, k}\big[\lVert \epsilon - \epsilon_\theta(a_k, k, h) \rVert^2\big]$$

This architecture provides the policy with the ability to translate highly variable multi-view sensory observations into robust and task-appropriate goals for manipulation, even amid occlusions and environmental variability.

2. Reduced and Manipulability-Aware Quadratic Programming (ReM-QP) Controller

M4Diffuser employs a Reduced Manipulability-aware QP (ReM-QP) controller for efficient and robust execution of the high-level goals provided by the diffusion policy. Classical holistic QP controllers include explicit slack variables $\delta$ to relax equality constraints, which increases computational complexity and can degrade motion smoothness.

The ReM-QP formulation eliminates slack variables via the substitution

$\delta = \nu^*_e - J \dot{x}$

where $\nu^*_e$ is the desired end-effector twist and $J$ is the manipulator Jacobian. This transforms the optimization problem to a convex quadratic program over joint velocities only:

$$\begin{align*} \text{minimize} \quad & \frac{1}{2} \dot{x}^\top Q_{\mathrm{red}} \dot{x} + c_{\mathrm{red}}^\top \dot{x} \ \text{subject to} \quad & A \dot{x} \leq b \end{align*}$$

with $$Q_{\mathrm{red}} = Q_{qq} + J^\top Q_{\delta\delta} J$$ and $$c_{\mathrm{red}} = c_q - J^\top Q_{\delta\delta} \nu^*_e$$.

Manipulability-awareness is incorporated using the inverse condition number (ICN) of the full-body Jacobian:

$$\mathrm{ICN}(q) = \frac{\sigma_{\min}(J)}{\sigma_{\max}(J)}$$

where $\sigma_{\min}$ and $\sigma_{\max}$ are the minimum and maximum singular values of $J$. The gradient $\nabla \mathrm{ICN}(q)$ is computed via centered finite differences and incorporated as a preference term in $c_q$, biasing the solution away from singular configurations and promoting smooth, robust operation.

3. Experimental Results: Simulation and Real-World Domains

Experimental validation of M4Diffuser demonstrates substantial improvements in task success rates, motion efficiency, and collision reduction over traditional and learning-driven baselines. In simulation, the elimination of slack variables yielded a decrease in task completion time (from 147 s to 84 s) and, when combined with ICN-based preference, improved smoothness (RMS jerk reduction by 35%). In real-world deployments—on the DARKO robot across reach, pick-and-place, and door-opening tasks in kitchen environments—M4Diffuser achieved an average success rate of 82.4% with collision rates as low as 6.8%. This constituted gains of 28% and 69% respectively over traditional pipelines.

A comparison table:

Method	Success Rate	Collision Rate
M4Diffuser	82.4%	6.8%
Holistic QP (base)	54.2%	22.0%
OMPL (classic)	40%–75%	14%–35%

The method's ability to generalize to novel objects (croissant, eggplant, etc.) and altered placements further highlights its robustness in unstructured settings.

4. Technical Innovations and Methodological Significance

M4Diffuser’s technical innovations include multi-view perception fused in a Transformer encoder, diffusion-driven goal generation in latent action space, and a manipulator controller that couples efficiency with singularity avoidance. Notably, the slack-variable elimination in the QP yields both computational speed and strict convexity, while the ICN gradient term dynamically adapts preferences to the current robot configuration.

The combination of these modules enables closed-loop, scene-aware operation whereby real-time visual and proprioceptive inputs directly generate and execute whole-body action trajectories, without decoupled perception-action stages or exhaustive trajectory optimization.

This framework advances scalable mobile manipulation by supporting adaptation in environments with dynamic obstacles, partial occlusion, and non-repeating task compositions.

5. Connections to Broader Diffuser Methodologies

M4Diffuser extends and interfaces with a spectrum of diffusion methodologies found in recent literature. Key relatives include:

• Efficient Transformer architectures employing diffusion over sparse graph patterns for sequence modeling (Feng et al., 2022)
• Mixture-of-Diffusers for spatial control in generative modeling (Jiménez, 2023)
• Multivariate diffusion models leveraging auxiliary variables for generative performance (Singhal et al., 2023)
• Sensor fusion by denoising diffusion processes in multi-modal robotics (Le et al., 6 Apr 2024)

These works collectively demonstrate how diffusion processes—initially formulated for generative tasks—can serve as the core of perception, inference, control, and fusion within both discrete data and continuous action spaces.

6. Generalization and Robustness in Unstructured Environments

The multi-view diffusion policy’s ability to fuse spatially distributed visual inputs and its iterative denoising in latent action space are instrumental for performance in unstructured domains. The reduced QP controller’s adaptation to manipulability mitigates risks near singularities. A plausible implication is that such architectures could be beneficial in other fields requiring coordinated control with partial or uncertain observations, such as search-and-rescue robotics, medical robotics, or adaptive assembly.

7. Supplementary Resources and Implementation

Comprehensive implementation details, experiment videos, and extended results—as well as the teleoperation pipeline for data collection—are available on the project website: https://sites.google.com/view/m4diffuser.

The dataset and method documentation there supplement the formal publication, supporting reproducibility and comparative analysis.

---

M4Diffuser, as formalized in (Dong et al., 18 Sep 2025), is a high-performance framework for mobile manipulation that integrates perception-driven, diffusion-based policy generation and an efficient, manipulability-resilient controller, yielding strong gains in efficiency, smoothness, and generalization across simulation and physical domains. The underlying principles and architectural advances link this system to a broad class of recent innovations in diffusion modeling, spanning control, data generation, sensor fusion, and inverse problem solving.