ConditionNET: Learning Preconditions and Effects for Execution Monitoring

TL;DR

ConditionNET is a novel data-driven approach that learns preconditions and effects of actions for robotic execution monitoring, improving anomaly detection and phase prediction in real-world tasks.

Contribution

We introduce ConditionNET, a vision-language model that explicitly models action dependencies, with optimized training for consistent features, and demonstrate its effectiveness on robotic datasets.

Findings

Outperforms baselines in anomaly detection and phase prediction

Successfully applied to real robot monitoring tasks

Collected a new dataset with successful and failed demonstrations

Abstract

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.