  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.
