A correlational analysis of multiagent sensorimotor interactions: clustering autonomous and controllable entities
This work addresses a foundational challenge in AI for developing social robots, but it is incremental as it primarily discusses measures and missing elements without presenting new methods or data.
The paper tackles the problem of enabling Theory of Mind (ToM) abilities in synthetic agents by proposing a dual-arm robotic setup to analyze sensorimotor interactions, aiming to distinguish self from others and identify intentions through correlation analysis, but does not report concrete experimental results or numbers.
A first step to reach Theory of Mind (ToM) abilities (attribution of beliefs to others) in synthetic agents through sensorimotor interactions, would be to tag sensory data with agent typology and action intentions: autonomous agent X moved an object under the box. We propose a dual arm robotic setup in which ToM could be probed. We then discuss what measures can be extracted from sensorimotor interaction data (based on a correlation analysis) in the proposed setup that allow to distinguish self than other and other/inanimate from other/active with intentions. We finally discuss what elements are missing in current cognitive architectures to be able to acquire ToM abilities in synthetic agents from sensorimotor interactions, bottom-up from reactive agent interaction behaviors and top-down from the optimization of social behaviour and cooperation.