Humans Predict Action using Grammar-like Structures.

ABSTRACT: Efficient action prediction is of central importance for the fluent workflow between humans and equally so for human-robot interaction. To achieve prediction, actions can be algorithmically encoded by a series of events, where every event corresponds to a change in a (static or dynamic) relation between some of the objects in the scene. These structures are similar to a context-free grammar and, importantly, within this framework the actual objects are irrelevant for prediction, only their relational changes matter. Manipulation actions and others can be uniquely encoded this way. Using a virtual reality setup and testing several different manipulation actions, here we show that humans predict actions in an event-based manner following the sequence of relational changes. Testing this with

SUBMITTER: Worgotter F 

PROVIDER: S-EPMC7055263 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature