SARA-RT: 自己適応型ロバストアテンションによるロボティクストランスフォーマーのスケーリング
SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention
ロボット搭載向けに、事前学習済みまたは微調整済みのトランスフォーマーポリシーを線形アテンションに変換する「アップトレーニング」を提案し、RT-2や点群トランスフォーマーを高速化した。
著者: Isabel Leal, Krzysztof Choromanski, Deepali Jain, Avinava Dubey, Jake Varley, Michael Ryoo, Yao Lu, Frederick Liu, Vikas Sindhwani, Quan Vuong, Tamas Sarlos, Ken Oslund, Karol Hausman, Kanishka Rao
分類: cs.RO, cs.AI
原文アブストラクト
We present Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT): a new paradigm for addressing the emerging challenge of scaling up Robotics Transformers (RT) for on-robot deployment. SARA-RT relies on the new method of fine-tuning proposed by us, called up-training. It converts pre-trained or already fine-tuned Transformer-based robotic policies of quadratic time complexity (including massive billion-parameter vision-language-action models or VLAs), into their efficient linear-attention counterparts maintaining high quality. We demonstrate the effectiveness of SARA-RT by speeding up: (a) the class of recently introduced RT-2 models, the first VLA robotic policies pre-trained on internet-scale data, as well as (b) Point Cloud Transformer (PCT) robotic policies operating on large point clouds. We complement our results with the rigorous mathematical analysis providing deeper insight into the phenomenon of SARA.
