# Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection

arXiv: https://arxiv.org/abs/2412.04455 · HTML: https://mlsanity.com/paper/2412.04455/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- Builds on 67 of its 88 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Builds on (most cited first, 30 of 67)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [Generation and Comprehension of Unambiguous Object Descriptions](https://mlsanity.com/paper/1511.02283/) (2015)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [LISA: Reasoning Segmentation via Large Language Model](https://mlsanity.com/paper/2308.00692/) (2023)
- [RLBench: The Robot Learning Benchmark & Learning Environment](https://mlsanity.com/paper/1909.12271/) (2019)
- [BridgeData V2: A Dataset for Robot Learning at Scale](https://mlsanity.com/paper/2308.12952/) (2023)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://mlsanity.com/paper/2109.12098/) (2021)
- [Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP](https://mlsanity.com/paper/2210.04150/) (2022)
- [Segment Everything Everywhere All at Once](https://mlsanity.com/paper/2304.06718/) (2023)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [Visual Programming: Compositional visual reasoning without training](https://mlsanity.com/paper/2211.11559/) (2022)
- [ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation](https://mlsanity.com/paper/2409.01652/) (2024)
- [ChatGPT for Robotics: Design Principles and Model Abilities](https://mlsanity.com/paper/2306.17582/) (2023)
- [Generalized Decoding for Pixel, Image, and Language](https://mlsanity.com/paper/2212.11270/) (2022)
- [GRES: Generalized Referring Expression Segmentation](https://mlsanity.com/paper/2306.00968/) (2023)

## Built on by (most cited first, 30 of 63)

- [RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete](https://mlsanity.com/paper/2502.21257/) (2025)
- [RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics](https://mlsanity.com/paper/2506.04308/) (2025)
- [T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation](https://mlsanity.com/paper/2501.12612/) (2025)
- [SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation](https://mlsanity.com/paper/2502.13143/) (2025)
- [RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction](https://mlsanity.com/paper/2505.12224/) (2025)
- [ManipLVM-R1: Reinforcement Learning for Reasoning in Embodied Manipulation with Large Vision-Language Models](https://mlsanity.com/paper/2505.16517/) (2025)
- [TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics](https://mlsanity.com/paper/2510.07181/) (2025)
- [Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain](https://mlsanity.com/paper/2510.17801/) (2025)
- [VIKI-R: Coordinating Embodied Multi-Agent Cooperation via Reinforcement Learning](https://mlsanity.com/paper/2506.09049/) (2025)
- [From Language to Locomotion: Retargeting-free Humanoid Control via Motion Latent Guidance](https://mlsanity.com/paper/2510.14952/) (2025)
- [RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic](https://mlsanity.com/paper/2512.21220/) (2025)
- [Prune4Web: DOM Tree Pruning Programming for Web Agent](https://mlsanity.com/paper/2511.21398/) (2025)
- [Towards Spatial Trace with Reasoning in Vision-Language Models for Robotics](https://mlsanity.com/paper/2512.13660/) (2025)
- [NavigateDiff: Visual Predictors are Zero-Shot Navigation Assistants](https://mlsanity.com/paper/2502.13894/) (2025)
- [SwiftSpec: Ultra-Low Latency LLM Decoding by Scaling Asynchronous Speculative Decoding](https://mlsanity.com/paper/2506.11309/) (2025)
- [Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models](https://mlsanity.com/paper/2512.01946/) (2025)
- [SaPaVe: Towards Active Perception and Manipulation in Vision-Language-Action Models for Robotics](https://mlsanity.com/paper/2603.12193/) (2026)
- [Self-Refining Vision Language Model for Robotic Failure Detection and Reasoning](https://mlsanity.com/paper/2602.12405/) (2026)
- [From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation](https://mlsanity.com/paper/2605.11951/) (2026)
- [PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution](https://mlsanity.com/paper/2607.16636/) (2026)
- [MathSticks: A Benchmark for Visual Symbolic Compositional Reasoning with Matchstick Puzzles](https://mlsanity.com/paper/2510.00483/) (2025)
- [VLCP: Vision Language Control Policy Closed-Loop Code Replanning for Robot Manipulation](https://mlsanity.com/paper/2608.16978/) (2026)
- [A Unified Framework for Real-Time Failure Handling in Robotics Using Vision-Language Models, Reactive Planner and Behavior Trees](https://mlsanity.com/paper/2503.15202/) (2025)
- [Sentinel-VLA: A Metacognitive VLA Model with Active Status Monitoring for Dynamic Reasoning and Error Recovery](https://mlsanity.com/paper/2605.01191/) (2026)
- [HybridGen: VLM-Guided Hybrid Planning for Scalable Data Generation of Imitation Learning](https://mlsanity.com/paper/2503.13171/) (2025)
- [EmbodiedCoder: Parameterized Embodied Mobile Manipulation via Modern Coding Model](https://mlsanity.com/paper/2510.06207/) (2025)
- [LiveSearchBench: An Automatically Constructed Benchmark for Retrieval and Reasoning over Dynamic Knowledge](https://mlsanity.com/paper/2511.01409/) (2025)
- [A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Robotics](https://mlsanity.com/paper/2505.12583/) (2025)
- [GauDP: Reinventing Multi-Agent Collaboration through Gaussian-Image Synergy in Diffusion Policies](https://mlsanity.com/paper/2511.00998/) (2025)
- [Beyond Success: Refining Elegant Robot Manipulation from Mixed-Quality Data via Just-in-Time Intervention](https://mlsanity.com/paper/2511.22555/) (2025)

## Abstract (arXiv)

Automatic detection and prevention of open-set failures are crucial in closed-loop robotic systems. Recent studies often struggle to simultaneously identify unexpected failures reactively after they occur and prevent foreseeable ones proactively. To this end, we propose Code-as-Monitor (CaM), a novel paradigm leveraging the vision-language model (VLM) for both open-set reactive and proactive failure detection. The core of our method is to formulate both tasks as a unified set of spatio-temporal constraint satisfaction problems and use VLM-generated code to evaluate them for real-time monitoring. To enhance the accuracy and efficiency of monitoring, we further introduce constraint elements that abstract constraint-related entities or their parts into compact geometric elements. This approach offers greater generality, simplifies tracking, and facilitates constraint-aware visual programming by leveraging these elements as visual prompts. Experiments show that CaM achieves a 28.7% higher success rate and reduces execution time by 31.8% under severe disturbances compared to baselines across three simulators and a real-world setting. Moreover, CaM can be integrated with open-loop control policies to form closed-loop systems, enabling long-horizon tasks in cluttered scenes with dynamic environments.