  Preference-based reinforcement learning (RL) has emerged as a new field in
robot learning, where humans play a pivotal role in shaping robot behavior by
expressing preferences on different sequences of state-action pairs. However,
formulating realistic policies for robots demands responses from humans to an
extensive array of queries. In this work, we approach the sample-efficiency
challenge by expanding the information collected per query to contain both
preferences and optional text prompting. To accomplish this, we leverage the
zero-shot capabilities of a large language model (LLM) to reason from the text
provided by humans. To accommodate the additional query information, we
reformulate the reward learning objectives to contain flexible highlights --
state-action pairs that contain relatively high information and are related to
the features processed in a zero-shot fashion from a pretrained LLM. In both a
simulated scenario and a user study, we reveal the effectiveness of our work by
analyzing the feedback and its implications. Additionally, the collective
feedback collected serves to train a robot on socially compliant trajectories
in a simulated social navigation landscape. We provide video examples of the
trained policies at https://sites.google.com/view/rl-predilect
