2020/10/19 by Xavier Puig, Tianmin Shu, Puig, Xavier +13 · 41 citations
Computer Science · Psychology · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Computer science #Data science #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Perception #Psychology #Reinforcement Learning in Robotics #Social Robot Interaction and HRI #Sociology #cs.AI #cs.LG #cs.MA
paper · pdf · doi:10.48550/arxiv.2010.09890
published in arXiv (Cornell University) (Cornell University) · ICLR 2021
openalex publication_date 2020/10/19 · openalex created_date 2020/10/29 · arxiv created 2021/05/03 · arxiv updated 2021/05/04 · openalex updated_date 2026/08/04
In this paper, we introduce Watch-And-Help (WAH), a challenge for testing social intelligence in agents. In WAH, an AI agent needs to help a human-like agent perform a complex household task efficiently. To succeed, the AI agent needs to i) understand the underlying goal of the task by watching a single demonstration of the human-like agent performing the same task (social perception), and ii) coordinate with the human-like agent to solve the task in an unseen environment as fast as possible (human-AI collaboration). For this challenge, we build VirtualHome-Social, a multi-agent household environment, and provide a benchmark including both planning and learning based baselines. We evaluate the performance of AI agents with the human-like agent as well as with real humans using objective metrics and subjective user ratings. Experimental results demonstrate that the proposed challenge and virtual environment enable a systematic evaluation on the important aspects of machine social intelligence at scale.