Article

Conditional Neural Processes for Self-Supervised Goal Discovery in Action Imitation

The study evaluates whether Conditional Neural Processes can autonomously learn action representations that are informative about underlying goals. It finds that adding an attention mechanism to the latent space enables robust goal representations to form early in the observation of an action sequence. The authors accordingly suggest the model could serve as a general self-supervised learner for robots. The work was presented as a poster at the IROS 2023 Workshop on World Models and Predictive Coding in Cognitive Robotics.

Part of the event: Conditional Neural Processes for Self-Supervised Goal Discovery in Action Imitation

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