Authors: Enshen Zhou, Qi Su, Cheng Chi, Zhizheng Zhang, Zhongyuan Wang, Tiejun Huang, Lu Sheng, He Wang
Abstract: 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.
Source: http://arxiv.org/abs/2412.04455v1
