  Intelligent agents need to autonomously navigate and interact within
contextual environments to perform a wide range of daily tasks based on
human-level instructions. These agents require a foundational understanding of
the world, incorporating common sense and knowledge, to interpret such
instructions. Moreover, they must possess precise low-level skills for movement
and interaction to execute the detailed task plans derived from these
instructions. In this work, we address the task of synthesizing continuous
human-object interactions for manipulating large objects within contextual
environments, guided by human-level instructions. Our goal is to generate
synchronized object motion, full-body human motion, and detailed finger motion,
all essential for realistic interactions. Our framework consists of a large
language model (LLM) planning module and a low-level motion generator. We use
LLMs to deduce spatial object relationships and devise a method for accurately
determining their positions and orientations in target scene layouts.
Additionally, the LLM planner outlines a detailed task plan specifying a
sequence of sub-tasks. This task plan, along with the target object poses,
serves as input for our low-level motion generator, which seamlessly alternates
between navigation and interaction modules. We present the first complete
system that can synthesize object motion, full-body motion, and finger motion
simultaneously from human-level instructions. Our experiments demonstrate the
effectiveness of our high-level planner in generating plausible target layouts
and our low-level motion generator in synthesizing realistic interactions for
diverse objects. Please refer to our project page for more results:
https://hoifhli.github.io/.
