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.
@article{kienle2024mutt,
title={MuTT: A Multimodal Trajectory Transformer for Robot Skills},
author={Kienle, Claudius and Alt, Benjamin and Celik, Onur and Becker, Philipp and Katic, Darko and J{\"a}kel, Rainer and Neumann, Gerhard},
journal={arXiv preprint arXiv:2407.15660},
year={2024}
}