  A robot in a human-centric environment needs to account for the human's
intent and future motion in its task and motion planning to ensure safe and
effective operation. This requires symbolic reasoning about probable future
actions and the ability to tie these actions to specific locations in the
physical environment. While one can train behavioral models capable of
predicting human motion from past activities, this approach requires large
amounts of data to achieve acceptable long-horizon predictions. More
importantly, the resulting models are constrained to specific data formats and
modalities. Moreover, connecting predictions from such models to the
environment at hand to ensure the applicability of these predictions is an
unsolved problem. We present a system that utilizes a Large Language Model
(LLM) to infer a human's next actions from a range of modalities without
fine-tuning. A novel aspect of our system that is critical to robotics
applications is that it links the predicted actions to specific locations in a
semantic map of the environment. Our method leverages the fact that LLMs,
trained on a vast corpus of text describing typical human behaviors, encode
substantial world knowledge, including probable sequences of human actions and
activities. We demonstrate how these localized activity predictions can be
incorporated in a human-aware task planner for an assistive robot to reduce the
occurrences of undesirable human-robot interactions by 29.2% on average.
