  Rather than traditional position control, impedance control is preferred to
ensure the safe operation of industrial robots programmed from demonstrations.
However, variable stiffness learning studies have focused on task performance
rather than safety (or compliance). Thus, this paper proposes a novel stiffness
learning method to satisfy both task performance and compliance requirements.
The proposed method optimizes the task and compliance objectives (T/C
objectives) simultaneously via multi-objective Bayesian optimization. We define
the stiffness search space by segmenting a demonstration into task phases, each
with constant responsible stiffness. The segmentation is performed by
identifying impedance control-aware switching linear dynamics (IC-SLD) from the
demonstration. We also utilize the stiffness obtained by proposed IC-SLD as
priors for efficient optimization. Experiments on simulated tasks and a real
robot demonstrate that IC-SLD-based segmentation and the use of priors improve
the optimization efficiency compared to existing baseline methods.
