1 research outputs found
That's Mine! Learning Ownership Relations and Norms for Robots
The ability for autonomous agents to learn and conform to human norms is
crucial for their safety and effectiveness in social environments. While recent
work has led to frameworks for the representation and inference of simple
social rules, research into norm learning remains at an exploratory stage.
Here, we present a robotic system capable of representing, learning, and
inferring ownership relations and norms. Ownership is represented as a graph of
probabilistic relations between objects and their owners, along with a database
of predicate-based norms that constrain the actions permissible on owned
objects. To learn these norms and relations, our system integrates (i) a novel
incremental norm learning algorithm capable of both one-shot learning and
induction from specific examples, (ii) Bayesian inference of ownership
relations in response to apparent rule violations, and (iii) percept-based
prediction of an object's likely owners. Through a series of simulated and
real-world experiments, we demonstrate the competence and flexibility of the
system in performing object manipulation tasks that require a variety of norms
to be followed, laying the groundwork for future research into the acquisition
and application of social norms.Comment: 9 pg., 2 fig., accepted for AAAI-2019. Video demo:
https://bit.ly/2z8obET GitHub: https://github.com/OwnageBot/ownage_bo