  It is crucial for robots to be aware of the presence of constraints in order
to acquire safe policies. However, explicitly specifying all constraints in an
environment can be a challenging task. State-of-the-art constraint inference
algorithms learn constraints from demonstrations, but tend to be
computationally expensive and prone to instability issues. In this paper, we
propose a novel Bayesian method that infers constraints based on preferences
over demonstrations. The main advantages of our proposed approach are that it
1) infers constraints without calculating a new policy at each iteration, 2)
uses a simple and more realistic ranking of groups of demonstrations, without
requiring pairwise comparisons over all demonstrations, and 3) adapts to cases
where there are varying levels of constraint violation. Our empirical results
demonstrate that our proposed Bayesian approach infers constraints of varying
severity, more accurately than state-of-the-art constraint inference methods.
