# ConditionNET: Learning Preconditions and Effects for Execution Monitoring

arXiv: https://arxiv.org/abs/2502.01167 · HTML: https://mlsanity.com/paper/2502.01167/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2025.

## Built on by (most cited first, 7 of 7)

- [RC-NF: Robot-Conditioned Normalizing Flow for Real-Time Anomaly Detection in Robotic Manipulation](https://mlsanity.com/paper/2603.11106/) (2026)
- [AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policies](https://mlsanity.com/paper/2608.07065/) (2026)
- [M2R2: MultiModal Robotic Representation for Temporal Action Segmentation](https://mlsanity.com/paper/2504.18662/) (2025)
- [Enhancing Video-Based Robot Failure Detection Using Task Knowledge](https://mlsanity.com/paper/2508.18705/) (2025)
- [Visual Anomaly Detection for Reliable Robotic Implantation of Flexible Microelectrode Array](https://mlsanity.com/paper/2510.09071/) (2025)
- [See and Switch: Vision-Based Branching for Interactive Robot-Skill Programming](https://mlsanity.com/paper/2603.08057/) (2026)
- [RoboVAD: A Large Cross-Domain Evaluation Benchmark for Anomaly Detection in Robotic Arm Manipulation Videos](https://mlsanity.com/paper/2609.17843/) (2026)

## Abstract (arXiv)

The introduction of robots into everyday scenarios necessitates algorithms capable of monitoring the execution of tasks. In this paper, we propose ConditionNET, an approach for learning the preconditions and effects of actions in a fully data-driven manner. We develop an efficient vision-language model and introduce additional optimization objectives during training to optimize for consistent feature representations. ConditionNET explicitly models the dependencies between actions, preconditions, and effects, leading to improved performance. We evaluate our model on two robotic datasets, one of which we collected for this paper, containing 406 successful and 138 failed teleoperated demonstrations of a Franka Emika Panda robot performing tasks like pouring and cleaning the counter. We show in our experiments that ConditionNET outperforms all baselines on both anomaly detection and phase prediction tasks. Furthermore, we implement an action monitoring system on a real robot to demonstrate the practical applicability of the learned preconditions and effects. Our results highlight the potential of ConditionNET for enhancing the reliability and adaptability of robots in real-world environments. The data is available on the project website: https://dsliwowski1.github.io/ConditionNET_page.