Source-linked AI summary
Holodeck: Language Guided Generation of 3D Embodied AI Environments
Yue Yang, Fan-Yun Sun, Luca Weihs, Eli VanderBilt, Alvaro Herrasti, Winson Han, Jiajun Wu, Nick Haber, Ranjay Krishna, Lingjie Liu, Chris Callison-Burch, Mark Yatskar, Aniruddha Kembhavi, Christopher Clark
TL;DR
Existing Embodied AI environments are labor-intensive to create, limiting the diversity and scope of available scenes. HOLODECK generates customized interactive environments from text using GPT-4, Objaverse assets, and constraint-based layout optimization. Human studies and navigation experiments show strong scene-generation quality and utility in novel environments, while asset coverage remains a boundary.
Problem
Manual environment creation requires expertise and extensive effort, restricting the diversity and scope of 3D scenes available for Embodied AI.
Method
HOLODECK uses GPT-4 to interpret text prompts, selects suitable Objaverse assets, and arranges them through modular construction and constraint-based layout optimization.
Results
Human evaluations favored HOLODECK over PROCTHOR for residential scenes and found high-quality outputs across scene types; agents trained on its scenes navigated better in novel expert-designed environments.
Takeaways & Limitations
Textual prompts provide broad control over diverse, customized, interactive scene generation and can support embodied agents adapting to new scene types and objects.
Takeaways & Limitations
Some requested unique assets, such as a dental x-ray machine, are unavailable in Objaverse, motivating expansion of the asset collection and more sophisticated layout algorithms.
Abstract
from arXiv · showhide
3D simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope. To mitigate this limitation, we present Holodeck, a system that generates 3D environments to match a user-supplied prompt fully automatedly. Holodeck can generate diverse scenes, e.g., arcades, spas, and museums, adjust the designs for styles, and can capture the semantics of complex queries such as "apartment for a researcher with a cat" and "office of a professor who is a fan of Star Wars". Holodeck leverages a large language model (i.e., GPT-4) for common sense knowledge about what the scene might look like and uses a large collection of 3D assets from Objaverse to populate the scene with diverse objects. To address the challenge of positioning objects correctly, we prompt GPT-4 to generate spatial relational constraints between objects and then optimize the layout to satisfy those constraints. Our large-scale human evaluation shows that annotators prefer Holodeck over manually designed procedural baselines in residential scenes and that Holodeck can produce high-quality outputs for diverse scene types. We also demonstrate an exciting application of Holodeck in Embodied AI, training agents to navigate in novel scenes like music rooms and daycares without human-constructed data, which is a significant step forward in developing general-purpose embodied agents.
1. Introduction
Existing Embodied AI environments are costly to construct and often limited in diversity, while text-to-3D approaches can introduce artifacts and lack interactivity. HOLODECK addresses these constraints by using an LLM and diverse assets to generate customized interactive scenes, with evaluations showing strong scene quality and navigation utility.
- Motivation: Manual design, scanning, and hard-coded procedural generation require substantial effort and have kept prior 3D environment work focused on limited scene types.Text-to-3D methods can produce mesh distortions and lack the interactivity required for Embodied AI.
- HOLODECK: HOLODECK uses GPT-4 to design floor plans, materials, doorways, windows, and coherent object arrangements from textual descriptions.Its constraint-based optimization arranges more than 50K Objaverse assets to satisfy environment descriptions.
- Capabilities: HOLODECK exploits LLM commonsense and spatial knowledge to generate diverse scene types and interpret specific prompts through relevant object placement.Examples include arcades, spas, museums, and placing an R2-D2 on a desk for a Star Wars fan.
- Capabilities: Text prompts support style customization, spatially organized floor plans, and control over scene creation across varied environment descriptions.Examples include Victorian-style scenes and offices for three professors connected by a long hallway.
- Evaluation: A 680-participant user study found that HOLODECK significantly surpassed PROCTHOR for residential scenes and produced high-quality outputs across scene types.Agents trained on HOLODECK scenes navigated better in expert-designed novel environments such as Daycare and Gym.
- Contributions: The paper contributes a language-guided generator, human validation of scene quality, and evidence that generated environments can support adaptation to new navigation scenes and objects.These contributions target diverse, customized, interactive environments for Embodied AI.
2. Related Work
Prior Embodied AI environments rely on manual design, scanning, or procedural rules, while recent LLM layout methods can produce physically implausible arrangements. HOLODECK combines modular scene construction, Objaverse retrieval, and constraint-based layout optimization to generate interactive environments from text.
- Embodied AI Environments: Embodied AI environments have been built by 3D artists, scans, and procedural frameworks, each presenting trade-offs in scalability, labor, interactivity, or scene similarity.PROCTHOR generates large-scale interactive environments, while Phone2Proc uses phone scans to create semantically similar training scenes.
- HOLODECK: Figure 2 depicts HOLODECK transforming text input into a 3D environment through multiple rounds of conversation with an LLM.The workflow is organized around floor, wall, doorway, window, object-selection, and layout-design operations.
- 3D Layout Generation: Directly asking LLMs for numerical layout values can create physically implausible scenes, including overlapping assets.HOLODECK instead samples spatial relational constraints and uses a solver to optimize the layout.
- Text-driven 3D Generation: Text-driven 3D generation has progressed from category-specific shapes and textures to zero-shot objects, but complex scene generation remains difficult.The passage contrasts object-generation strengths with limitations on complex 3D scenes.
3. HOLODECK
HOLODECK converts textual descriptions into diverse, customized, interactive 3D environments through specialized language-guided modules. Its layout pipeline combines LLM-generated spatial constraints with optimization, while Objaverse supplies a large, annotated asset library.
- System overview: HOLODECK uses specialized modules to generate floor plans, materials, doorways, windows, and coherent object arrangements from textual descriptions.The prompting framework gives each module a task description, output format, and one-shot example.
- Floor and wall design: GPT-4 proposes room coordinates, realistic dimensions, connectivity, and prompt-appropriate multi-room floor plans.A minimum-area instruction of 9 m2 can prevent overly small rooms.
- Floor and wall design: HOLODECK selects from 236 materials with 148 colors and supports semantic floor-and-wall customization.Examples include concrete materials for prison cells and requested pink, brick, or checkered textures.
- Door and window design: The doorway and window module matches designs to 40 door styles and 21 window types, with adjustable size, height, quantity, and related properties.The system can generate wider wheelchair-accessible doors and multiple floor-to-ceiling sunroom windows.
- Object selection: The object-selection module retrieves suitable assets using descriptions, dimensions, visual similarity, and textual similarity from a library of 51,464 annotated assets.HOLODECK can place objects on floors, walls, other objects, and ceilings.
4. Human Evaluation
HOLODECK is evaluated through human studies and CLIP-based visual coherence measures across residential and diverse indoor scenes. Results show stronger residential preferences, broad scene coverage, and benefits from constraint-based layout design, while complex layouts and unavailable assets remain limitations.
- 680 graduate students evaluated HOLODECK in residential comparison, diverse-scene quality, and layout ablation studies.
- Comparative Analysis on Residential Scenes: 64.4% of annotators preferred HOLODECK overall, with preferences also favoring its asset selection at 59.8% and layout coherence at 56.9% over PROCTHOR.
- Comparative Analysis on Residential Scenes: HOLODECK’s CLIP scores exceeded PROCTHOR and closely approached human-designed iTHOR scenes, indicating visual coherence with designated residential scene types.
- HOLODECK on Diverse Scenes: 28 out of 52 diverse scene types received higher human preference scores for HOLODECK than for PROCTHOR.
- HOLODECK on Diverse Scenes: HOLODECK struggles with complex layouts such as restaurants and scenes requiring unique assets unavailable in Objaverse.
- Ablation Study on Layout Design: Constraint-based layouts significantly outperformed alternative methods on bathrooms, bedrooms, and living rooms, while matching EDGE on kitchens.
- Ablation Study on Layout Design: ABSOLUTE layouts performed no better than RANDOM because collisions and boundary errors were typically rated poorly by humans.
5. Object Navigation in Novel Environments
HOLODECK is applied to ObjectNav by synthesizing training environments for novel scene types and testing whether agents can adapt beyond household benchmarks. The setup compares pretrained and scene-specific fine-tuned models, including models using HOLODECK-selected Objaverse assets.
- Existing ObjectNav benchmarks cover household environments and only 16 object types, motivating the NOVELTYTHOR novel-scene evaluation.
- The application tests whether synthetic scenes can better match a novel testing distribution for ObjectNav agents.
- HOLODECK automatically generates 100 scenes for each novel scene type, while +OBJAVERSE augments PROCTHOR with HOLODECK’s scene-specific object selection.
- HOLODECK-generated layouts place semantically related music-room objects, such as pianos, violin cases, and cellos, in close proximity.
6. Conclusion and Limitation
The paper presents HOLODECK as a language-guided system for generating diverse, interactive Embodied AI environments and evaluates both scene quality and object-navigation utility. It identifies adding more assets and exploring broader applications as future directions.
- HOLODECK generates diverse and interactive Embodied AI environments from textual descriptions using large language model guidance.
- The authors assess scene quality with large-scale human evaluation and validate utility through object navigation in novel scenes.
- Future work will add more 3D assets and explore broader Embodied AI applications.
A.1. Efficiency and Cost
HOLODECK’s generation cost and runtime scale with the number of rooms and include both language-model API calls and layout optimization. The implementation reports an approximate per-room cost and single-room generation time.
- Generating an interactive house with k rooms requires 3+3×k API calls.
- Using gpt-4-1106-preview costs approximately $0.2 per room.
- A single room takes about 3 minutes to generate on a MacBook with an M1 chip, including API calls and layout optimization.
- The Floor Module outputs room type, floor material, wall material, and four corner-coordinate tuples for each room.
A.3. Doorway & Window Modules
HOLODECK uses language-model-specified doorway and window attributes to select and place architectural elements from diverse asset collections.
- HOLODECK uses 40 doors and 21 windows from PROCTHOR to support diverse doorway and window construction.
- The LLM specifies connected rooms, connection type, size, and door style for doorway selection.CLIP selects the door whose image most closely matches the description.
- HOLODECK includes examples of varied doors and windows in Figures 13 and 14.
- The LLM specifies room type, wall direction, window type, size, quantity, and height for window placement.
A.4. Object Selection Module
HOLODECK retrieves repository assets by matching LLM-proposed object descriptions and sizes against textual, visual, and geometric metadata.
- Each Objaverse asset stores a textual description, 3D bounding-box dimensions, and images captured from three angles.
- LLM-proposed object descriptions and bounding-box sizes provide the retrieval targets for matching repository assets.
- HOLODECK evaluates candidate assets using visual similarity, textual similarity, and size matching.Visual similarity uses CLIP, while textual similarity helps keep retrieval within the correct category.
- The overall matching score is a weighted combination of the retrieval metrics, with α = 100, β = 1, and γ = 10.The asset with the highest matching score is selected.
A.5. Layout Design Module
HOLODECK converts language-model spatial relations into solver-based object layouts while supporting asset annotation, surface placement, and runtime scene management.
- The layout module applies LLM-provided spatial constraints such as edge, near, in front of, alignment, and face-to relations.For example, a coffee table can be placed in the middle, in front of, facing, and near a sofa.
- Floor-object placement uses DFS and MILP solvers to optimize layouts under geometric constraints.
- The DFS solver searches grid placements, rejects collisions and boundary violations, and selects solutions satisfying the most soft constraints.
- MILP translates layout constraints into linear form, treating distance relations as objective terms while applying other specified constraints as hard constraints.
- Small surface objects are positioned with LLM-proposed placements and AI2-THOR’s RandomSpawn function.
- GPT-4-V annotates assets from four orthogonal images to improve object retrieval and placement accuracy.
- Caching and unloading systems manage repeated asset use and thousands of runtime 3D objects.
- HOLODECK supports external text-to-3D assets, Unity rendering for efficiency, and Blender rendering for improved realism.
B. Qualitative Examples
Supplementary examples demonstrate HOLODECK’s scene coverage, rendering options, and infrastructure for generating and integrating diverse embodied environments.
- HOLODECK presents 20 additional scene types selected from the MIT dataset and distinct from the main-paper examples.
- Unity is the default renderer for efficient embodied-agent training, while Blender can improve scene realism.
- HOLODECK can address GPT-4’s cultural bias through prompting.
- Figure 24 compares five layout-design methods, while Figure 25 compares residential scenes from iTHOR, PROCTHOR, and HOLODECK.
- NOVELTYTHOR contains human-designed scenes intended to challenge embodied agents with diverse environments and Objaverse assets.
- The asset pipeline generates Unity prefabs and associated resources for ten NOVELTYTHOR scenes across five categories.
D. Cultural Bias
HOLODECK’s cultural biases can arise from both its language model and 3D asset retrieval, with culturally specific prompts sometimes eliciting stereotypical objects. Prompt refinement can help reduce these effects, but is unlikely to eliminate them fully.
- Biases in HOLODECK generation can stem from both the LLM and the 3D asset retrieval component.
- Prompts containing culturally specific terms such as “Japanese” may disproportionately produce prototypical objects like Manga posters.
- Adding a suffix such as “no cultural bias” or making the prompt more detailed can significantly help mitigate cultural bias.The authors state that this strategy is unlikely to fully remove bias.