VertiCoder：垂直に険しい地形における自己教師あり運動力学表現学習
VertiCoder: Self-Supervised Kinodynamic Representation Learning on Vertically Challenging Terrain
マスク付きパッチ再構成による自己教師あり学習で、垂直に険しい地形でのロボット移動に関する4つの下流タスク（順・逆運動力学、模倣学習、パッチ再構成）を単一表現で扱えるTransformerベースの手法を提案。
著者: Mohammad Nazeri, Aniket Datar, Anuj Pokhrel, Chenhui Pan, Garrett Warnell, Xuesu Xiao
分類: cs.RO
原文アブストラクト
We present VertiCoder, a self-supervised representation learning approach for robot mobility on vertically challenging terrain. Using the same pre-training process, VertiCoder can handle four different downstream tasks, including forward kinodynamics learning, inverse kinodynamics learning, behavior cloning, and patch reconstruction with a single representation. VertiCoder uses a TransformerEncoder to learn the local context of its surroundings by random masking and next patch reconstruction. We show that VertiCoder achieves better performance across all four different tasks compared to specialized End-to-End models with 77% fewer parameters. We also show VertiCoder's comparable performance against state-of-the-art kinodynamic modeling and planning approaches in real-world robot deployment. These results underscore the efficacy of VertiCoder in mitigating overfitting and fostering more robust generalization across diverse environmental contexts and downstream vehicle kinodynamic tasks.
