MuTT: A Multimodal Trajectory Transformer for Robot Skills
<|reference_start|>MuTT: A Multimodal Trajectory Transformer for Robot Skills: 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.<|reference_end|>
@article{kienle2024mutt:,
    title={MuTT: A Multimodal Trajectory Transformer for Robot Skills},
    author={Claudius Kienle, Benjamin Alt, Onur Celik, Philipp Becker, Darko
  Katic, Rainer J"akel and Gerhard Neumann},
    journal={arXiv preprint arXiv:2407.15660},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15660},
    primaryClass={cs.RO cs.LG}
}