Efficient Human-Machine Work Transfer Through Latent Structure Decomposition
When humans delegate tasks---whether to human workers or robots---they do so either to trade money for time, or to leverage additional knowledge and capabilities. For complex tasks, however, describing the work to be done requires substantial effort, which reduces the benefit to the requester who delegates tasks. On one hand, human workers---e.g., crowd workers, friends or colleagues on social network, factory workers---have diverse knowledge and level of commitment, making it difficult to achieve joint efforts towards the requester's goal. In contrast, robots and machines have clearly defined capabilities and full commitment, but the requester lacks an efficient way to coordinate them for flexible workflows.
This dissertation presents a series of workflows and systems to enable efficient work transfer to human workers or robots. First, I present BlueSky, a system that can automatically coordinate hundreds of crowd workers to enumerate ideas for a given topic. The latent structure of the idea enumeration task is decomposed into a three-step workflow to guide the crowd workers. Second, I present CoStory, a system that requests alternative designs from friends or colleagues by decomposing the design task into hierarchical chunks. Third, I present AdapTutAR, a system that delegates machine operation tasks to workers through adaptive Augmented Reality tutorials. Finally, I present Vipo, a system that allows requesters to customize tasks for robots and smart machines through spatial-visual programming. This dissertation demonstrates that decomposing latent task structure enables task delegation in an on-demand, scalable, and distributed way.