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Diffusion Models for Robotic Manipulation: A Survey

Rosa Wolf, Yitian Shi, Sheng Liu, Rania Rayyes

arXiv:2504.08438v3cs.ROstat.ML

TL;DR

Robotic manipulation lacks a consolidated account of how diffusion models are designed, trained, and evaluated across tasks. This survey classifies methods spanning architectures, learning frameworks, and applications, finding strong multimodal and high-dimensional modeling capabilities while identifying slow inference and limited generalizability as persistent challenges.

  • Problem

    The literature on diffusion models for robotic manipulation spans architectures, learning frameworks, applications, and evaluations without a consolidated survey.

  • Method

    The survey systematically reviews diffusion models for trajectory generation, grasp synthesis, visual data augmentation, and their integration with imitation and reinforcement learning.

  • Results

    Diffusion models show strong multimodal modeling, training stability, and robustness to high-dimensional inputs and outputs across robotic manipulation applications.

  • Takeaways & Limitations

    The reviewed methods support trajectory, grasping, and visual-manipulation applications, including high-resolution 2D and 3D visual observations.

  • Takeaways & Limitations

    The field still faces limited generalizability and slow inference, while lifelong-learning research remains sparsely explored and sometimes restricted to simulation or predefined object descriptions.

Abstract

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Diffusion generative models have demonstrated remarkable success in visual domains such as image and video generation. They have also recently emerged as a promising approach in robotics, especially in robot manipulations. Diffusion models leverage a probabilistic framework, and they stand out with their ability to model multi-modal distributions and their robustness to high-dimensional input and output spaces. This survey provides a comprehensive review of state-of-the-art diffusion models in robotic manipulation, including grasp learning, trajectory planning, and data augmentation. Diffusion models for scene and image augmentation lie at the intersection of robotics and computer vision for vision-based tasks to enhance generalizability and data scarcity. This paper also presents the two main frameworks of diffusion models and their integration with imitation learning and reinforcement learning. In addition, it discusses the common architectures and benchmarks and points out the challenges and advantages of current state-of-the-art diffusion-based methods.

1 Introduction

Diffusion models are increasingly used in robotic manipulation because they can represent multimodal solutions in high-dimensional settings. The survey organizes this literature by architecture, learning framework, application, and evaluation, covering trajectory generation, grasp synthesis, and visual data augmentation.

  • Motivation: Diffusion models can represent multimodal distributions, although practical experiments indicate they may remain biased toward specific modes.Multimodality refers to capturing multiple distinct feasible outcomes, such as alternative trajectories.
  • Applications: Since 2022, diffusion models have been applied to robotic manipulation tasks including trajectory planning and grasp prediction.Their multimodal modeling ability is useful when manipulation tasks admit multiple equally valid solutions.
  • Motivation: Capturing redundant manipulation solutions can improve generalizability and versatility across object placements and inference constraints.The survey describes both imitation-learning and reinforcement-learning integrations, with trajectory planning primarily using imitation learning.
  • Survey scope: The survey presents the first systematic review focused on diffusion models in robotic manipulation, classifying methods by architecture, learning framework, application, and evaluation.It also provides illustrative taxonomies.
  • Survey scope: The survey covers diffusion-model fundamentals, common architectures, trajectory generation, robotic grasp synthesis, and visual data augmentation.These topics are organized across the paper’s background, architecture, and application sections.

2 Preliminaries on Diffusion Models

Diffusion models transform data into simple noise through a forward process and generate samples by reversing that process. The preliminaries distinguish score-based models and DDPMs, then discuss iterative sampling costs and methods that trade sampling speed against quality.

  • Core framework: Diffusion models gradually perturb an unknown data distribution toward a simple known distribution, then iteratively reverse the perturbations to generate samples.The forward and backward processes are illustrated for images, trajectories, and grasp poses.
  • Model families: Score-based models learn gradients of perturbed data log-density, while DDPMs train a noise-prediction network conditioned on the noise scale.Both approaches generate samples by starting from noise and iteratively denoising.
  • DDPM: In DDPM training, data are progressively degraded toward N(0, I), while closed-form noising permits uniform sampling of a noise scale and direct construction of the degraded sample.The noise-prediction network is trained by minimizing mean squared error across noise scales.
  • DDPM: DDPM sampling starts from x_K ∼ N(0, I) and repeatedly predicts earlier states until x_0 is obtained, which is approximately distributed as the true data when steps are sufficiently small.The reverse transition is modeled as Gaussian under the stated small-step condition.
  • Sampling limitations: Iterative sampling makes diffusion models relatively slow, and reducing noise levels accelerates generation at the cost of sample quality.This motivates architectural and sampling adaptations targeting both speed and quality.
  • Sampling improvements: DDIM uses deterministic sampling and can outperform DDPM with only a few sampling iterations, while later solvers modify solver order or step-size schedules.The survey notes that original comparisons of DDIM and DPM-solver used image-classification benchmarks, motivating broader validation.
  • Alternative formulations: Flow matching learns a time-dependent velocity field directly, simplifying training and avoiding a noise schedule, but few-step sampling can risk mode collapse and infeasible solutions.The survey describes step-size conditioning and self-consistency as one response to this risk.

3 Architecture

Diffusion models for robotic manipulation use CNNs, transformers, or MLPs as denoising networks, with architecture choice balancing representation capacity, precision, efficiency, and inference cost. Temporal U-Nets, conditioning mechanisms, receding-horizon control, and reduced DDIM sampling support trajectory generation under robotics constraints.

  • Architectures: CNNs, transformers, and MLPs are the three predominant architectures for diffusion denoising networks.Temporal U-Nets are the most frequently employed CNN-based architecture in robotic manipulation.
  • Convolutional neural networks: Temporal U-Nets replace two-dimensional spatial convolutions with one-dimensional temporal convolutions for robotic trajectories.This adaptation leverages the U-Net architecture’s reported sample efficiency and generalization with small training datasets.
  • Convolutional neural networks: Diffusion Policy generates action trajectories rather than jointly denoising state and action trajectories, conditioning on observation history through FiLM.The formulation supports additional and multiple conditions, including constraints embedded with an MLP.
  • Trajectory generation: Receding-horizon control repeatedly denoises subtrajectories, executes only the control horizon Hc ≤ H, and replans after advancing time.The trajectory is conditioned on past observations while the inner loop iteratively lowers the noise level before execution.
  • Architecture comparison: CNNs show lower hyperparameter sensitivity and slightly higher success rates on some complex positional-control tasks, whereas transformers better capture high-frequency trajectories and long-range dependencies.The comparison indicates that architecture advantages depend on task characteristics and control requirements.
  • Architecture comparison: MLPs provide computational efficiency and higher-rate sampling, while U-Nets and transformers consume more resources and may have longer inference times.Transformers are described as powerful for high-dimensional inputs and outputs, whereas MLPs are commonly used in reinforcement learning because of training stability.
  • Sampling steps: DDIM commonly reduces inference to five to ten sampling steps after training with 50-100 noise levels, trading sample quality against inference time.One study documented a slight performance decline when DDIM sampling steps were reduced to 10%.

4 Applications

Diffusion models are applied in robotic manipulation through trajectory generation, robotic grasping, and visual data augmentation for vision-based manipulation.

  • Trajectory generation: Trajectory generation is a dominant application of diffusion models in robotic manipulation.
  • Robotic grasping: Robotic grasping is a dominant application of diffusion models in robotic manipulation.
  • Visual data augmentation: Visual data augmentation is used for vision-based robotic manipulation.

4.1 Trajectory generation

The survey reviews diffusion-based trajectory generation across imitation learning and reinforcement learning, emphasizing multimodal action modeling, visual generalization, and constraints. It also identifies practical limits involving embodiment, continual adaptation, offline data, simulation dependence, and real-world evaluation.

  • Imitation learning: Diffusion policies model smooth action trajectories conditioned on sensory observations, supporting flexible behavior beyond demonstrated scenarios.This generative formulation is presented as a way to learn visuomotor policies for task generalization.
  • Embodiment: Most diffusion manipulation methods use parallel grippers or simpler end-effectors, while relatively few address dexterous manipulation.
  • Imitation learning: Visual diffusion methods outperform approaches using only 2D visual information on more complex tasks and remain robust under adversarial lighting.
  • Multi-task and VLA planning: VLA–diffusion systems use VLAs for coarse actions and diffusion policies to refine actions, improving precision and adaptability across robot embodiments.The survey contrasts this with autoregressive action-token generation, whose slow inference and quantization errors limit real-time precision.
  • Constrained planning: Constraint guidance steers diffusion training but does not guarantee constraint satisfaction; stronger methods report simulated gains from reverse-process constraint tightening.The reported constraint-tightening evaluation uses a single simulated setup, leaving broader validation open.
  • Offline reinforcement learning: Diffusion reinforcement-learning methods commonly combine offline policies and critics, including skill composition for long-horizon tasks and Q-learning-based objectives.Research on online and offline-to-online diffusion reinforcement learning remains limited.
  • Offline reinforcement learning: Offline diffusion reinforcement learning improves sample efficiency and training stability but requires high-quality reward-labeled data, can overfit, and cannot react to distribution shifts.Compared with imitation learning, it may better model diverse state-action spaces but is more prone to overfitting suboptimal behavior.
  • Evaluation scope: Many reinforcement-learning methods rely on ground-truth environment information available mainly in simulation, and only a few are specifically engineered for robotic manipulation.

4.2 Robotic grasp generation

Diffusion-based grasp generation spans explicit SE(3) pose diffusion, latent-space diffusion, and language-guided methods. A central challenge is preserving valid non-Euclidean grasp transformations while modeling diverse feasible grasps.

  • Grasp diffusion methods include explicit 6-DoF pose diffusion on SE(3), latent-space diffusion, and language-guided task-oriented generation.
  • Standard Euclidean diffusion is difficult to extend to SE(3) because numerical instability and non-Euclidean geometry complicate rotations and translations.
  • SE(3)-Diff applies Lie-group score matching within an energy-based model to bridge diffusion in R6 and SE(3), while directly evaluating grasp quality.
  • Latent grasp diffusion uses VAEs to model grasp features conditioned on point clouds and task latents, without explicitly enforcing the SE(3) constraint.
  • SE(3) bi-equivariance preserves spatial and geometric relationships when transformations are applied to inputs and generated grasp poses.

4.3 Visual data Augmentation

Diffusion models augment visual data for manipulation through semantic image editing, viewpoint reconstruction, and language-conditioned scene arrangement. These methods address data scarcity and incomplete sensing, but scene reconstruction remains limited by computational cost and complete occlusion.

  • Visual augmentation uses pretrained diffusion models for dataset scaling, scene reconstruction, and scene rearrangement in vision-based manipulation.
  • Diffusion inpainting changes object colors, textures, or identities and can augment both manipulation objects and backgrounds.
  • Most augmentation methods modify observations rather than actions, using pretrained language, vision-language, or text-to-image models.
  • Single-view sensing produces incomplete point clouds or images, with occlusion and sensor errors making grasp and trajectory prediction difficult.
  • Diffusion models reconstruct new object viewpoints from RGBD images and camera intrinsics by projecting known points and inpainting missing data.
  • Scene reconstruction is computationally costly, uncommon in robotic manipulation, and unable to recover completely occluded objects.
  • Text-to-image diffusion can generate target object arrangements from initial visual observations and language prompts for tasks such as table setting or counter clearing.

5 Experiments and Benchmarks

The survey evaluates diffusion methods across established simulation and real-world manipulation benchmarks, using both conventional and diffusion-based baselines. Most methods include simulation and real-world evaluation, with some transferring simulation-trained policies zero shot.

  • Evaluation details and benchmark references are organized in separate tables for imitation learning, reinforcement learning, grasp learning, and the appendix.
  • Common benchmarks include CALVIN, RLBench, RelayKitchen, Meta-World, D4RL Kitchen, FurnitureBench, Adroit, and LIBERO.
  • Baselines often are not diffusion-based, while SE(3)-Diffusion Policy, Diffuser, Diffusion-QL, and Decision Diffuser serve as recurring diffusion baselines.
  • Most methods are evaluated in both simulation and real-world experiments, and some simulation-trained policies transfer zero shot using domain randomization or scene reconstruction.

6 Conclusion, Limitations and Outlook

The survey identifies generalizability and inference speed as persistent challenges for diffusion models in robotic manipulation. It highlights data dependence, slow iterative sampling, and underexplored continual learning as directions for future work.

  • Generalizability: Generalizability remains a prevalent limitation across robotic-manipulation methodologies, including variation in object types, lighting conditions, and task complexity.
  • Generalizability: Trajectory methods relying on imitation learning inherit dependence on training-data quality and diversity, making out-of-distribution situations difficult under covariate shift.
  • Generalizability: Data scaling can improve generalizability but typically requires large training datasets and substantial computational resources.
  • Sampling speed: Iterative diffusion sampling is time-intensive, impeding efficiency and real-time prediction capabilities.
  • Sampling speed: DDIM sampling was reported as ten times faster with a 5.6% decrease in task performance, but the significance of this trade-off is task-dependent.
  • Conclusion: The survey reviews trajectory generation, robotic grasping, and visual data augmentation while emphasizing diffusion models’ multimodal modeling and high-dimensional robustness.
  • Outlook: Fast samplers developed in computer vision have not yet been evaluated in robotic manipulation, leaving sampling efficiency an open challenge.
  • Outlook: Continual learning is proposed as a promising but widely unexplored approach for improving generalizability and adaptability in dynamic, unfamiliar environments.

Funding

The paper acknowledges funding from the Deutsche Forschungsgemeinschaft.

  • The work was funded by the Deutsche Forschungsgemeinschaft through SFB-1574, grant 471687386.

Appendix

The appendix documents benchmarks for trajectory diffusion under imitation and reinforcement learning, along with architecture and dataset references.

  • Table 6 records imitation-learning trajectory-diffusion benchmarks, including demonstrations, diffusion baselines, and real-world evaluation status.
  • Table 7 records reinforcement-learning trajectory-diffusion benchmarks, including simulation, real-world evaluation, demonstrations, and diffusion baselines.
  • Table 8 lists encoder architectures for different input modalities.
  • Table 9 lists datasets and corresponding references for trajectory diffusion and grasp diffusion.
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