運転用VLAの異なる車体構成へのゼロショット転移に向けて
Towards Zero-Shot Transfer Across Embodiments For Driving VLAs
運転タスクのVLAモデルにおいて、複数データセットでの訓練とBEV-Forcing補助タスクが、未見のカメラ構成へのゼロショット転移性能を向上させることを示した。ただし、訓練データの多様性を増やすと補助タスクの効果は減少する。
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著者: Caio Azevedo, Stefano Sabatini, Sascha Hornauer, Fabien Moutarde
分類: cs.CV
原文アブストラクト
Vision-Language-Action models (VLAs) have shown strong potential in autonomous driving by leveraging multimodal pretraining for instruction following, visual reasoning, and scene-level generalization. In robotic manipulation, scaling VLA fine-tuning across multiple robot setups--especially when unifying representations across embodiments--has been shown to improve in-dataset performance and cross-embodiment generalization; in autonomous driving, however, VLAs remain largely trained on individual datasets and are rarely evaluated for zero-shot transfer to unseen datasets and camera rigs; furthermore naively adding more datasets to the training data does not necessarily lead to better performance within seen embodiments. To address these problems, we study multi-dataset training for the driving task and BEV-Forcing, an auxiliary objective that transfers ground-plane object-layout information from a specialized Bird's-Eye-View model into the VLA backbone. By encouraging the model to represent object position through a shared BEV spatial interface, we show that an auxiliary task such as BEV-Forcing can improve both in-distribution and out-of-distribution performance when training on a small number of camera rigs. As the number of training embodiments increases, however, the benefits of the auxiliary task are reduced; we present this as evidence that new techniques in the literature may see their benefits diminish when simply scaling up training diversity, which motivates presenting results taking into account data scaling.