Humans Predict Action using Grammar-like Structures.

Scientific Reports
F WörgötterM Tamosiunaite

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 chained actions, we measure the percentage predictive temporal gain for humans and compare it to action-chains performed by robots showing that the gain is approximately equal. Event-based and, thus, object independent action recognition and prediction may be important for cognitively deducing properties of unknown objects seen in action, helping to address bootstrapping of object knowledge especially in infants.

References

Aug 22, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Abhinav GuptaLarry S Davis
Nov 23, 2011·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Katerina Pastra, Yiannis Aloimonos
Dec 15, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Hema S Koppula, Ashutosh Saxena

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Citations

Dec 17, 2020·Frontiers in Neurorobotics·Carlo MichaelisChristian Tetzlaff

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Software Mentioned

Vive VR
ESEC
SEC

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