  When a robot autonomously performs a complex task, it frequently must balance
competing objectives while maintaining safety. This becomes more difficult in
uncertain environments with stochastic outcomes. Enhancing transparency in the
robot's behavior and aligning with user preferences are also crucial. This
paper introduces a novel framework for multi-objective reinforcement learning
that ensures safe task execution, optimizes trade-offs between objectives, and
adheres to user preferences. The framework has two main layers: a
multi-objective task planner and a high-level selector. The planning layer
generates a set of optimal trade-off plans that guarantee satisfaction of a
temporal logic task. The selector uses active inference to decide which
generated plan best complies with user preferences and aids learning. Operating
iteratively, the framework updates a parameterized learning model based on
collected data. Case studies and benchmarks on both manipulation and mobile
robots show that our framework outperforms other methods and (i) learns
multiple optimal trade-offs, (ii) adheres to a user preference, and (iii)
allows the user to adjust the balance between (i) and (ii).
