Visit www.benjamins.com

A dynamic field approach to goal inference, error detection and anticipatory action selection in human-robot collaboration

MyBook is a cheap paperback edition of the original book and will be sold at uniform, low price.
This Chapter is currently unavailable for purchase.
Abstract

In this chapter we present results of our ongoing research on efficient and fluent human-robot collaboration that is heavily inspired by recent experimental findings about the neurocognitive mechanisms supporting joint action in humans. The robot control architecture implements the joint coordination of actions and goals as a dynamic process that integrates contextual cues, shared task knowledge and the predicted outcome of the user’s motor behavior. The architecture is formalized as a coupled system of dynamic neural fields representing a distributed network of local but connected neural populations with specific functionalities. We validate the approach in a task in which a robot and a human user jointly construct a toy ‘vehicle’. We show that the context-dependent mapping from action observation onto appropriate complementary actions allows the robot to cope with dynamically changing joint action situations. More specifically, the results illustrate crucial cognitive capacities for efficient and successful human-robot collaboration such as goal inference, error detection and anticipatory action selection.

References

/content/books/9789027283399-10bic
dcterms_subject,pub_keyword
6
3
Loading
This is a required field
Please enter a valid email address