MuTT: ロボットスキルのためのマルチモーダル軌道トランスフォーマー
MuTT: A Multimodal Trajectory Transformer for Robot Skills
視覚・軌道・スキルパラメータを統合し、環境に応じたロボットスキルの実行を予測するエンコーダ・デコーダ型トランスフォーマーを提案。モデルベース最適化と組み合わせることで実環境実行なしにスキルパラメータを調整できる。
著者: Claudius Kienle, Benjamin Alt, Onur Celik, Philipp Becker, Darko Katic, Rainer Jäkel, Gerhard Neumann
分類: cs.RO, cs.LG
原文アブストラクト
High-level robot skills represent an increasingly popular paradigm in robot programming. However, configuring the skills' parameters for a specific task remains a manual and time-consuming endeavor. Existing approaches for learning or optimizing these parameters often require numerous real-world executions or do not work in dynamic environments. To address these challenges, we propose MuTT, a novel encoder-decoder transformer architecture designed to predict environment-aware executions of robot skills by integrating vision, trajectory, and robot skill parameters. Notably, we pioneer the fusion of vision and trajectory, introducing a novel trajectory projection. Furthermore, we illustrate MuTT's efficacy as a predictor when combined with a model-based robot skill optimizer. This approach facilitates the optimization of robot skill parameters for the current environment, without the need for real-world executions during optimization. Designed for compatibility with any representation of robot skills, MuTT demonstrates its versatility across three comprehensive experiments, showcasing superior performance across two different skill representations.
