  We address motion generation for high-DoF robot arms in complex settings with
obstacles, via points, etc. A significant advancement in this domain is
achieved by integrating Learning from Demonstration (LfD) into the motion
generation process. This integration facilitates rapid adaptation to new tasks
and optimizes the utilization of accumulated expertise by allowing robots to
learn and generalize from demonstrated trajectories.
  We train a transformer architecture on a large dataset of simulated
trajectories. This architecture, based on a conditional variational autoencoder
transformer, learns essential motion generation skills and adapts these to meet
auxiliary tasks and constraints. Our auto-regressive approach enables real-time
integration of feedback from the physical system, enhancing the adaptability
and efficiency of motion generation. We show that our model can generate motion
from initial and target points, but also that it can adapt trajectories in
navigating complex tasks, including obstacle avoidance, via points, and meeting
velocity and acceleration constraints, across platforms.
