  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.
