# Phone2Proc: Bringing Robust Robots Into Our Chaotic World

arXiv: https://arxiv.org/abs/2212.04819 · HTML: https://mlsanity.com/paper/2212.04819/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 77 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 30 of 57)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [ProcTHOR: Large-Scale Embodied AI Using Procedural Generation](https://mlsanity.com/paper/2206.06994/) (2022)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects](https://mlsanity.com/paper/2006.13171/) (2020)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience](https://mlsanity.com/paper/1810.05687/) (2018)
- [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017)
- [Semi-parametric Topological Memory for Navigation](https://mlsanity.com/paper/1803.00653/) (2018)
- [Neural Topological SLAM for Visual Navigation](https://mlsanity.com/paper/2005.12256/) (2020)
- [RoboTHOR: An Open Simulation-to-Real Embodied AI Platform](https://mlsanity.com/paper/2004.06799/) (2020)
- [Simple but Effective: CLIP Embeddings for Embodied AI](https://mlsanity.com/paper/2111.09888/) (2021)
- [ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings](https://mlsanity.com/paper/2206.12403/) (2022)
- [GNM: A General Navigation Model to Drive Any Robot](https://mlsanity.com/paper/2210.03370/) (2022)
- [Ditto: Building Digital Twins of Articulated Objects from Interaction](https://mlsanity.com/paper/2202.08227/) (2022)
- [Sim-to-Real Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/1806.07851/) (2018)
- [Visual Semantic Navigation using Scene Priors](https://mlsanity.com/paper/1810.06543/) (2018)
- [Rapid Exploration for Open-World Navigation with Latent Goal Models](https://mlsanity.com/paper/2104.05859/) (2021)
- [Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale](https://mlsanity.com/paper/2204.03514/) (2022)
- [Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks](https://mlsanity.com/paper/1903.03878/) (2019)
- [ManipulaTHOR: A Framework for Visual Object Manipulation](https://mlsanity.com/paper/2104.11213/) (2021)

## Built on by (most cited first, 26 of 26)

- [Holodeck: Language Guided Generation of 3D Embodied AI Environments](https://mlsanity.com/paper/2312.09067/) (2023)
- [Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation](https://mlsanity.com/paper/2403.03949/) (2024)
- [URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images](https://mlsanity.com/paper/2405.11656/) (2024)
- [Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation](https://mlsanity.com/paper/2505.02836/) (2025)
- [FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning](https://mlsanity.com/paper/2409.16578/) (2024)
- [SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World](https://mlsanity.com/paper/2312.02976/) (2023)
- [PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies](https://mlsanity.com/paper/2512.16881/) (2025)
- [LiteReality: Graphics-Ready 3D Scene Reconstruction from RGB-D Scans](https://mlsanity.com/paper/2507.02861/) (2025)
- [RobotArena ∞: Scalable Robot Benchmarking via Real-to-Sim Translation](https://mlsanity.com/paper/2510.23571/) (2025)
- [Harmonic Mobile Manipulation](https://mlsanity.com/paper/2312.06639/) (2023)
- [ReALFRED: An Embodied Instruction Following Benchmark in Photo-Realistic Environments](https://mlsanity.com/paper/2407.18550/) (2024)
- [GRS: Generating Robotic Simulation Tasks from Real-World Images](https://mlsanity.com/paper/2410.15536/) (2024)
- [EmbodiedSplat: Personalized Real-to-Sim-to-Real Navigation with Gaussian Splats from a Mobile Device](https://mlsanity.com/paper/2509.17430/) (2025)
- [Robot Learning with Super-Linear Scaling](https://mlsanity.com/paper/2412.01770/) (2024)
- [Seeing the Unseen: Visual Common Sense for Semantic Placement](https://mlsanity.com/paper/2401.07770/) (2024)
- [DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes](https://mlsanity.com/paper/2410.02730/) (2024)
- [UrbanVerse: Scaling Urban Simulation by Watching City-Tour Videos](https://mlsanity.com/paper/2510.15018/) (2025)
- [ReaDy-Go: Real-to-Sim Dynamic 3D Gaussian Splatting Simulation for Environment-Specific Visual Navigation with Moving Obstacles](https://mlsanity.com/paper/2602.11575/) (2026)
- [One-shot Video Imitation via Parameterized Symbolic Abstraction Graphs](https://mlsanity.com/paper/2408.12674/) (2024)
- [From Seeing to Simulating: Generative High-Fidelity Simulation with Digital Cousins for Generalizable Robot Learning and Evaluation](https://mlsanity.com/paper/2604.15805/) (2026)
- [MoK-RAG: Mixture of Knowledge Paths Enhanced Retrieval-Augmented Generation for Embodied AI Environments](https://mlsanity.com/paper/2503.13882/) (2025)
- [GARDEN: Gravity-Aligned Reconstruction of Disentangled ENvironments from RGB images](https://mlsanity.com/paper/2606.03921/) (2026)
- [Semantic Radiance Fields as Simulators for Spatial Reasoning in Real-World Scenes](https://mlsanity.com/paper/2608.13095/) (2026)
- [LiteReality-Agent: An Agentic System for Interactable 3D Indoor Scene Reconstruction](https://mlsanity.com/paper/2610.01863/) (2026)
- [EnvDreamer: Large-Scale Multimodal-to-Environment Generation for Embodied AI](https://mlsanity.com/paper/2610.04301/) (2026)
- [From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents](https://mlsanity.com/paper/2610.19420/) (2026)

## Abstract (arXiv)

Training embodied agents in simulation has become mainstream for the embodied AI community. However, these agents often struggle when deployed in the physical world due to their inability to generalize to real-world environments. In this paper, we present Phone2Proc, a method that uses a 10-minute phone scan and conditional procedural generation to create a distribution of training scenes that are semantically similar to the target environment. The generated scenes are conditioned on the wall layout and arrangement of large objects from the scan, while also sampling lighting, clutter, surface textures, and instances of smaller objects with randomized placement and materials. Leveraging just a simple RGB camera, training with Phone2Proc shows massive improvements from 34.7% to 70.7% success rate in sim-to-real ObjectNav performance across a test suite of over 200 trials in diverse real-world environments, including homes, offices, and RoboTHOR. Furthermore, Phone2Proc's diverse distribution of generated scenes makes agents remarkably robust to changes in the real world, such as human movement, object rearrangement, lighting changes, or clutter.