HAZARD Challenge: Embodied Decision Making in Dynamically Changing
  Environments
Qinhong Zhou Sunli Chen Yisong Wang Haozhe Xu Weihua Du Hongxin Zhang Yilun Du Joshua B. Tenenbaum Chuang Gan
Recent advances in high-fidelity virtual environments serve as one of the
major driving forces for building intelligent embodied agents to perceive,
reason and interact with the physical world. Typically, these environments
remain unchanged unless agents interact with them. However, in real-world
scenarios, agents might also face dynamically changing environments
characterized by unexpected events and need to rapidly take action accordingly.
To remedy this gap, we propose a new simulated embodied benchmark, called
HAZARD, specifically designed to assess the decision-making abilities of
embodied agents in dynamic situations. HAZARD consists of three unexpected
disaster scenarios, including fire, flood, and wind, and specifically supports
the utilization of large language models (LLMs) to assist common sense
reasoning and decision-making. This benchmark enables us to evaluate autonomous
agents' decision-making capabilities across various pipelines, including
reinforcement learning (RL), rule-based, and search-based methods in
dynamically changing environments. As a first step toward addressing this
challenge using large language models, we further develop an LLM-based agent
and perform an in-depth analysis of its promise and challenge of solving these
challenging tasks. HAZARD is available at https://vis-www.cs.umass.edu/hazard/.