Building a manual interaction database populated with physics-based models

The goal of this project is to create an incrementally growing database of manual interactions to help put manual intelligence research on a firmer empirical bases. In order to populate such a database necessitates the study of manual interactions in humans. The database should be populated with multimodal information: geometry information, tactile sensor information, vision information and sound information. Using these multimodal information sources will allow models to be built that represent manual interaction. The database can then be used to aid robots carry out complex tasks of the type that humans perform with ease. For as long as the idea of robots has been around, researchers have been studying ways to empower them to be able to interact with their environment in an intelligent way. A large portion of a humans interaction with the environment is done using our hands. We are studying ways to enable robots perform tasks with their hands that we perform with ease. Studying patterns of manual interactions in humans should provide useful insights into how such actions are performed. Using this knowledge we will develop a database of manual interactions that can be used to aid robots in carrying out complex tasks humans find easy. The areas of computational linguistics and natural language processing has benefited from databases such a Wordnet. However, a similar database does not exist for manual interactions. The goal of this project, therefore, is to create an incrementally growing database of manual interactions to help put manual intelligence research on a firmer empirical bases. In order to decide on the structure of the database involves the answering of several important scientific questions: How should manual interactions be represented for storage, comparison and retrieval? What are suitable similarity measures for manual interactions? What are the elementary building blocks of a manual interaction? How do manual interactions motivated on the perceptual, control and task levels differ? Solving these questions will involve using skills in both psychology and computer science.[view:groupmembers==194]