Source-linked AI summary
DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics
Ivan Kapelyukh, Vitalis Vosylius, Edward Johns
TL;DR
The paper addresses how robots can predict appropriate object arrangements without collecting task-specific arrangement data. DALL-E-Bot uses a pre-trained web-scale diffusion model to turn a single scene observation into a human-like goal image and execute it through object matching and pose alignment. The system achieves zero-shot, open-set, and autonomous rearrangement, with encouraging human and objective evaluations.
Problem
Existing goal-pose methods rely on arrangement examples and are difficult to scale across the diverse objects and scenes found in unstructured environments.
Method
DALL-E-Bot converts a scene into object descriptions, prompts DALL-E to generate candidate human-like goal images, matches objects across images, and executes the estimated poses.
Results
DALL-E-Bot performs rearrangement zero-shot without further data collection or training, supports open-set objects and scenes, and receives high user scores while outperforming heuristic baselines.
Takeaways & Limitations
Web-scale diffusion models can serve as imagination engines that provide robots with human-like arrangement preferences for autonomous rearrangement.
Takeaways & Limitations
The experiments focus on 3-DoF rearrangement, while generated objects can sometimes be difficult to align across real and diffusion-generated images.
Abstract
from arXiv · showhide
We introduce the first work to explore web-scale diffusion models for robotics. DALL-E-Bot enables a robot to rearrange objects in a scene, by first inferring a text description of those objects, then generating an image representing a natural, human-like arrangement of those objects, and finally physically arranging the objects according to that goal image. We show that this is possible zero-shot using DALL-E, without needing any further example arrangements, data collection, or training. DALL-E-Bot is fully autonomous and is not restricted to a pre-defined set of objects or scenes, thanks to DALL-E's web-scale pre-training. Encouraging real-world results, with both human studies and objective metrics, show that integrating web-scale diffusion models into robotics pipelines is a promising direction for scalable, unsupervised robot learning.
I. INTRODUCTION
Object rearrangement requires predicting poses that are semantically appropriate, aesthetically appealing, physically stable, and convenient, but example-based methods scale poorly to diverse environments. DALL-E-Bot addresses this challenge with a web-scale diffusion model that generates human-like goal arrangements zero-shot and autonomously.
- Motivation: Object rearrangement tasks require selecting and physically executing a goal pose for every object.Examples include setting tables, tidying offices, and packing groceries.
- Motivation: Goal arrangements must balance semantic appropriateness, aesthetic appeal, physical stability, and human convenience.
- Limitations of prior work: Prior goal-pose methods rely on example arrangements and are difficult to scale to unstructured environments with diverse objects and many acceptable arrangements.Existing methods often require hundreds or thousands of examples.
- Motivation: Web images provide abundant examples of human-arranged scenes, motivating the use of web-scale diffusion models to generalize arrangement preferences across objects and scenes.DALL-E 2 was trained on hundreds of millions of image-caption pairs from the Web.
- DALL-E-Bot: DALL-E-Bot uses a modular pipeline that converts an observed scene into a natural goal image and then executes the corresponding rearrangement with a robot.The pipeline includes object-centric representation, diffusion-based generation, and real-world execution.
- DALL-E-Bot: The framework is zero-shot, open-set, and autonomous, requiring no demonstrations, task-specific training, predefined object set, user-specified goal, or supervision.
II. RELATED WORK
Prior rearrangement work predicts goal states using discrete choices, learned pose distributions, demonstrations, or handcrafted priors, often requiring example arrangements. DALL-E-Bot instead exploits existing web-scale diffusion models for zero-shot rearrangement and can automatically generate realistic goal images without manual goal specification.
- Predicting Goal Arrangements: Prior methods predict goal states through discrete placement classification, learned continuous poses, demonstrations, or handcrafted priors.
- Predicting Goal Arrangements: Many continuous-pose methods depend on datasets of example arrangements, which can restrict them to particular object sets and scenes.
- Diffusion-based goal generation: DALL-E-Bot uses existing web-scale diffusion models to perform rearrangement without collecting or training on rearrangement examples.
- Autonomous goal specification: Automatically generated realistic goal images can complement existing manipulation methods by removing the need for users to provide a goal image.
C. Diffusion Models
The paper uses a general-purpose web-scale image diffusion model to generate human-like goal images from object descriptions and an initial scene image. These generated images are converted into object poses through semantic matching and mask alignment for robot execution.
- Diffusion Models: Unlike prior robotics uses that train diffusion models for control or fine-tune them on demonstrations, this framework uses a pre-trained image diffusion model zero-shot for object rearrangement.
- Diffusion Models: DALL-E 2 generates a goal image depicting a human-like arrangement from a text description of the scene.
- Diffusion Models: The pipeline converts the initial RGB observation into object captions, segmentation masks, and CLIP visual-semantic features before generating candidate goal images.
- Diffusion Models: Generated images are selected by object count and semantic similarity, then corresponding masks are aligned with ICP to estimate transformations for rearrangement.
B. Object-Level Representation
DALL-E-Bot builds an object-level scene representation, extracts object classes for prompting, generates candidate arrangements, and uses semantic and geometric correspondence to support physical rearrangement.
- Object-Level Representation: Mask R-CNN supplies object detections and masks, while OFA captions object crops because predicted class labels can be inaccurate.
- Object-Level Representation: The object-level representation contains each object’s segmentation mask, text caption, and semantic feature vector.
- Goal Image Generation: DALL-E samples candidate goal images from its conditional distribution, with image masks used to preserve fixed objects and ground the generated scene.
- Goal Image Generation: The prompt uses object-class nouns extracted from captions rather than visual characteristics, leaving detailed appearance conditioning for future work.
D. Image Selection & Object Matching
DALL-E-Bot selects a feasible generated goal image by filtering unsuitable samples, matching objects across images, aligning their masks, and adjusting for scale before robot execution.
- Generated images are filtered when object counts differ, movable objects overlap, or artefacts and extra objects hinder correspondence.If no image passes, another batch is sampled.
- Objects are matched between the initial and generated scenes using segmentation masks and CLIP semantic features.The matching problem is non-trivial because generated objects may be different instances.
- ICP aligns corresponding masks to estimate each object's 3-DoF pixel transformation from initial pose to goal pose.Multiple random initialisations address local optima, including symmetric-mask ambiguities.
- Object spacing is adjusted for scale mismatch so generated arrangements avoid collisions and excessive separation.Objects are moved closer or farther apart according to the size difference between generated and real objects.
- A depth camera projects pixel-space poses into 3D, after which the robot executes pick-and-place actions using intermediate poses when collisions would occur.The planner temporarily moves collision-causing objects aside before placing them at their goals.
IV. EXPERIMENTS
The experiments evaluate whether DALL-E-Bot can create human-like arrangements through subjective and objective metrics in real-world robot settings.
- DALL-E-Bot is evaluated using both subjective and objective metrics for human-like arrangement quality.
- Predicted arrangements are autonomously created on a 54x54 cm tabletop using a 7-DoF Franka Emika robot with a compliant suction gripper.The robot performs top-down pick-and-place operations, allowing evaluation of accumulated pipeline errors.
- The evaluation records the completed real-world arrangement as the outcome users would observe after the robot executes its actions.
1 Capture image II of initial, disorganised scene
The pipeline captures the initial scene, describes its objects textually, samples diffusion-generated goal images, and screens them before matching objects.
- The system obtains masks, captions, and CLIP vectors for each object detected in the initial image.The algorithm labels these as M, c, and v from Mask R-CNN, OFA, and CLIP.
- It constructs a scene-level text description from the detected object captions and samples a batch of goal images conditioned on that description and the initial image.
- Each generated image is parsed into object masks, captions, and CLIP vectors before feasibility checks are applied.
- The method computes pairwise object similarities and uses Hungarian optimisation to obtain the optimal correspondence between initial and generated objects.
10 Select goal image IG with max-similarity matches
The system selects and executes a generated arrangement through object-wise alignment, real-world planning, and comparisons against zero-shot baselines and variants.
- 10 Select goal image IG with max-similarity matches: For each matched object, the pipeline runs ICP from multiple initialisations and computes a pixel-space transformation between initial and goal masks.
- IV. EXPERIMENTS: Experiments cover dining, office, and fruit tabletop scenes with fixed and movable objects.
- IV. EXPERIMENTS: Because existing dataset-based methods are not zero-shot, comparisons use Rand-No-Coll, Geometric, DALL-E-Bot-AR, and DALL-E-Bot-NF.
- IV. EXPERIMENTS: DALL-E-Bot receives high user scores, outperforms heuristic baselines, and performs better than the no-filtering variant across scenes.The autoregressive variant generally performs well but struggles in dining scenes because thin cutlery can slip and accumulate error.
- IV. EXPERIMENTS: Diffusion models can occasionally generate surreal or unnatural images, motivating sample-and-filter selection of feasible goal images.
C. Placing Missing Objects with Inpainting
The experiment tests whether DALL-E-Bot can infer a suitable pose for a masked object while preserving the rest of a human-made arrangement. It compares the method with two zero-shot heuristics and finds that DALL-E-Bot places missing objects more accurately across users.
- The experiment masks one object from a human-made dining arrangement and asks DALL-E-Bot to predict its pose while keeping fixed objects unchanged.DALL-E-Bot inpaints the missing object, then compares its predicted pose with the user’s actual pose.
- The method is compared against Rand-No-Coll, which randomly places the missing object without collisions, and Geometric, which uses fixed-object centroids and orientation.Both baselines are zero-shot heuristics.
- Position and orientation errors are measured against the closest acceptable user pose, using the median across users because acceptable poses are multimodal.The table reports median errors rather than means to reduce sensitivity to outliers.
- DALL-E-Bot outperforms both baselines and accurately places missing objects for different users.The result indicates that inpainting conditions the prediction on the poses of other objects, enabling collaborative arrangement by humans and robots.
V. DISCUSSION
The discussion identifies three practical boundaries: the experiments use top-down 3-DoF rearrangement, prohibit movable-object overlap, and rely on imperfect cross-domain object alignment.
- The experiments focus on top-down pick-and-place with 3-DoF rearrangement, while 6-DoF poses and more complex interactions remain future work.The authors give shelf stacking as an example requiring more complex manipulation.
- The method assumes movable objects cannot overlap, so arrangements such as placing a fork on a plate are excluded.Future systems could plan stacking order and spread objects out initially to reduce occlusions.
- Cross-domain alignment can be unreliable because generated objects may lack visual details such as legible keyboard text.The authors expect improved diffusion models and alignment techniques to mitigate this issue.
B. Future Work
Future work considers user preferences, prompt engineering, and language-conditioned rearrangement. The conclusion frames web-scale diffusion models as a promising basis for autonomous, human-like robot rearrangement.
- Personal preferences: DALL-E may implicitly condition inpainting on visible user preferences, such as left- or right-handedness.Future work could condition on preferences inferred from scenes previously arranged by users.
- Prompt engineering: Adding prompt terms such as “neat, precise, ordered, geometric” improved the apparent neatness of dining-scene generations.The authors identify substantial scope for further prompt-engineering research.
- Language-conditioned rearrangement: User instructions can be added to prompts to request alternatives such as stacked or laid-out plates.Following spatial relations remains difficult for some diffusion models.
- Conclusions: Web-scale diffusion models can serve as robot “imagination engines,” enabling zero-shot, open-set, and autonomous rearrangement without additional data collection or training.Human studies found users happy with everyday rearrangement results, while inpainting helped condition arrangements on pre-placed objects.