EURECA: Enhanced Understanding of Real Environments via Crowd Assistance


  • Sai Gouravajhala University of Michigan – Ann Arbor
  • Jinyeong Yim University of Michigan – Ann Arbor
  • Karthik Desingh University of Michigan – Ann Arbor
  • Yanda Huang University of Michigan – Ann Arbor
  • Odest Jenkins University of Michigan – Ann Arbor
  • Walter Lasecki University of Michigan – Ann Arbor



Indoor robots hold the promise of automatically handling mundane daily tasks, helping to improve access for people with disabilities, and providing on-demand access to remote physical environments. Unfortunately, the ability to understand never-before-seen objects in scenes where new items may be added (e.g., purchased) or altered (e.g., damaged) on a regular basis remains an open challenge for robotics.  In this paper, we introduce EURECA, a mixed-initiative system that leverages online crowds of human contributors to help robots robustly identify 3D point cloud segments corresponding to user-referenced objects in near real-time. EURECA allows robots to understand multi-object 3D scenes on-the-fly (in ~40 seconds) by providing groups of non-expert crowd workers with intelligent tools that can segment objects more quickly (~70% faster) and more accurately (6% higher F1 score) than individuals. More broadly, EURECA introduces the first real-time crowdsourcing tool that addresses the challenge of learning about new objects in real-world settings, creating a new source of data for training robots online, as well as a platform for studying mixed-initiative crowdsourcing workflows for understanding 3D scenes.




How to Cite

Gouravajhala, S., Yim, J., Desingh, K., Huang, Y., Jenkins, O., & Lasecki, W. (2018). EURECA: Enhanced Understanding of Real Environments via Crowd Assistance. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 6(1), 31-40.