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.

翻译：在不确定和动态环境中进行机器人技能学习与执行是一项具有挑战性的任务。本文提出了一种结合模仿学习、环境状态预测与高层决策的自适应框架。主动适应机制避免了被动适应，后者往往滞后于环境变化而非预先应对。我们提出了一种新颖的模仿学习表征方法——弹性拉普拉斯轨迹编辑，该方法能依据对未来状态的预测持续调整轨迹形态。随后，采用无迹卡尔曼滤波与隐马尔可夫模型的高层反应系统，基于离散决策集防止在动态环境当前状态下执行不安全操作。我们首先在仿真中验证了模仿学习表征方法，随后通过腿式移动机械臂在36个真实场景中对完整框架进行了实验评估。结果表明，所提框架在不同环境动态变化下均能有效运行，产生鲁棒且稳定的自适应行为。