An Adaptive Framework for Manipulator Skill Reproduction in Dynamic Environments
<|reference_start|>An Adaptive Framework for Manipulator Skill Reproduction in Dynamic Environments: Robot skill learning and execution in uncertain and dynamic environments is a challenging task. This paper proposes an adaptive framework that combines Learning from Demonstration (LfD), environment state prediction, and high-level decision making. Proactive adaptation prevents the need for reactive adaptation, which lags behind changes in the environment rather than anticipating them. We propose a novel LfD representation, Elastic-Laplacian Trajectory Editing (ELTE), which continuously adapts the trajectory shape to predictions of future states. Then, a high-level reactive system using an Unscented Kalman Filter (UKF) and Hidden Markov Model (HMM) prevents unsafe execution in the current state of the dynamic environment based on a discrete set of decisions. We first validate our LfD representation in simulation, then experimentally assess the entire framework using a legged mobile manipulator in 36 real-world scenarios. We show the effectiveness of the proposed framework under different dynamic changes in the environment. Our results show that the proposed framework produces robust and stable adaptive behaviors.<|reference_end|>
@article{donald2024an,
    title={An Adaptive Framework for Manipulator Skill Reproduction in Dynamic
  Environments},
    author={Ryan Donald, Brendan Hertel, Stephen Misenti, Yan Gu, Reza Azadeh},
    journal={arXiv preprint arXiv:2405.15711},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15711},
    primaryClass={cs.RO}
}