Smart Help: Strategic Opponent Modeling for Proactive and Adaptive Robot
  Assistance in Households
Zhihao Cao Zidong Wang Siwen Xie Anji Liu Lifeng Fan
Despite the significant demand for assistive technology among vulnerable
groups (e.g., the elderly, children, and the disabled) in daily tasks, research
into advanced AI-driven assistive solutions that genuinely accommodate their
diverse needs remains sparse. Traditional human-machine interaction tasks often
require machines to simply help without nuanced consideration of human
abilities and feelings, such as their opportunity for practice and learning,
sense of self-improvement, and self-esteem. Addressing this gap, we define a
pivotal and novel challenge Smart Help, which aims to provide proactive yet
adaptive support to human agents with diverse disabilities and dynamic goals in
various tasks and environments. To establish this challenge, we leverage
AI2-THOR to build a new interactive 3D realistic household environment for the
Smart Help task. We introduce an innovative opponent modeling module that
provides a nuanced understanding of the main agent's capabilities and goals, in
order to optimize the assisting agent's helping policy. Rigorous experiments
validate the efficacy of our model components and show the superiority of our
holistic approach against established baselines. Our findings illustrate the
potential of AI-imbued assistive robots in improving the well-being of
vulnerable groups.