センサモータ事前学習によるロボット学習
Robot Learning with Sensorimotor Pre-training
カメラ画像・固有感覚・行動の系列をトークン化しマスク予測で自己教師あり事前学習するTransformerモデルRPTを提案し、実世界2万軌道で事前学習がスクラッチ学習を上回りタスク・環境・ロボット間の転移を可能にすることを示した。
著者: Ilija Radosavovic, Baifeng Shi, Letian Fu, Ken Goldberg, Trevor Darrell, Jitendra Malik
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. Given a sequence of camera images, proprioceptive robot states, and actions, we encode the sequence into tokens, mask out a subset, and train a model to predict the missing content from the rest. We hypothesize that if a robot can predict the masked-out content it will have acquired a good model of the physical world that can enable it to act. RPT is designed to operate on latent visual representations which makes prediction tractable, enables scaling to larger models, and allows fast inference on a real robot. To evaluate our approach, we collected a dataset of 20,000 real-world trajectories over 9 months using a combination of motion planning and grasping algorithms. We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
