  Humans demonstrate a variety of interesting behavioral characteristics when
performing tasks, such as selecting between seemingly equivalent optimal
actions, performing recovery actions when deviating from the optimal
trajectory, or moderating actions in response to sensed risks. However,
imitation learning, which attempts to teach robots to perform these same tasks
from observations of human demonstrations, often fails to capture such
behavior. Specifically, commonly used learning algorithms embody inherent
contradictions between the learning assumptions (e.g., single optimal action)
and actual human behavior (e.g., multiple optimal actions), thereby limiting
robot generalizability, applicability, and demonstration feasibility. To
address this, this paper proposes designing imitation learning algorithms with
a focus on utilizing human behavioral characteristics, thereby embodying
principles for capturing and exploiting actual demonstrator behavioral
characteristics. This paper presents the first imitation learning framework,
Bayesian Disturbance Injection (BDI), that typifies human behavioral
characteristics by incorporating model flexibility, robustification, and risk
sensitivity. Bayesian inference is used to learn flexible non-parametric
multi-action policies, while simultaneously robustifying policies by injecting
risk-sensitive disturbances to induce human recovery action and ensuring
demonstration feasibility. Our method is evaluated through risk-sensitive
simulations and real-robot experiments (e.g., table-sweep task, shaft-reach
task and shaft-insertion task) using the UR5e 6-DOF robotic arm, to demonstrate
the improved characterisation of behavior. Results show significant improvement
in task performance, through improved flexibility, robustness as well as
demonstration feasibility.
