FIT-MAC: A Closed-Loop Multi-Agent Framework for Robust Vision-Language-Action Models
To systematically mitigate this issue, we propose FIT-MAC, a failure-aware, closed-loop multi-agent control framework for improving the robustness of VLA policies at inference time.
ProofPaper ↗
Key points
- Vision-language-action (VLA) models have achieved strong performance on standard robotic manipulation benchmarks.
- Perturbations in object positions and task instructions may lead to complete task failure.
- FIT-MAC augments a VLA policy with an inference-time control layer.
- First, the Planner Agent parses the language instruction into a structured task specification that captures the manipulation goal and spatial constraints, and the Grounder Agent resolves each instruction-level reference to a visual target.
Sources (1)
- [1]FIT-MAC: A Closed-Loop Multi-Agent Framework for Robust Vision-Language-Action ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 12:21 PM
To systematically mitigate this issue, we propose FIT-MAC, a failure-aware, closed-loop multi-agent control framework for improving the robustness of VLA policies at inference time.
Vision-language-action (VLA) models have achieved strong performance on standard robotic manipulation benchmarks.
Extractive summary: sentences quoted from the sources.
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