Co-VLA: 視覚言語行動モデルのためのコンセンサスベース連合学習
Co-VLA: Consensus-based Federated Training for Vision-Language-Action Models
ロボットデータを分散したまま、ADMMによるコンセンサス最適化で視覚言語行動モデル(VLA)を連合学習し、集中学習と同等の性能を達成した。
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著者: Haolong Li, Guner Dilsad Er, Michael Muehlebach, Joerg Stueckler
分類: cs.RO
原文アブストラクト
Vision-language-action models (VLAs) have emerged as a promising paradigm for general-purpose robot learning, with performance improving as models and datasets scale. Scaling robot data collection, however, remains challenging because data are naturally distributed across robots, tasks, and locations, making centralization costly or impractical. Federated learning offers a way to train on decentralized robot data, but applying it to VLAs requires accounting for heterogeneous robot client data distributions. We present Co-VLA, which applies consensus optimization using the Alternating Direction Method of Multipliers~(ADMM) to federated VLA training. We show that the same algorithm supports both full-model training and parameter-efficient fine-tuning with both fixed-rank and rank-adaptive adapters. The name Co-VLA reflects both consensus and collaboration: clients with different local robot datasets collaboratively train a shared model without sharing their data. Our experiments demonstrate that Co-VLA achieves performance comparable to centralized training in both full-model training and parameter-efficient fine-tuning settings.