Guided Decoding for Robot Motion Generation and Adaption
<|reference_start|>Guided Decoding for Robot Motion Generation and Adaption: 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.<|reference_end|>
@article{chen2024guided,
    title={Guided Decoding for Robot On-line Motion Generation and Adaption},
    author={Nutan Chen, Botond Cseke, Elie Aljalbout, Alexandros Paraschos, Marvin
  Alles, Patrick van der Smagt},
    journal={arXiv preprint arXiv:2403.15239},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15239},
    primaryClass={cs.RO cs.LG}
}