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.

我们通过集成演示学习（LfD）到运动生成过程中，在复杂环境中（包括障碍物、通过点等）解决了高自由度机械臂的运动生成问题。我们通过在大规模模拟轨迹数据集上训练一个基于条件变分自动编码器变压器的结构，学习了关键的运动生成技能，并将其适应辅助任务和约束条件。我们的自回归方法实现了来自物理系统的实时反馈整合，增强了运动生成的适应性和效率。我们表明，我们的模型可以从初始点和目标点生成运动，同时也能够适应复杂任务的轨迹规划，包括障碍物避让、通过点和速度、加速度限制，在多种平台上实现。