That's Mine! Learning Ownership Relations and Norms for Robots
This addresses the challenge of social norm learning for robots, which is crucial for safety and effectiveness in human environments, though it is incremental as it builds on existing frameworks for social rules.
The paper tackles the problem of enabling robots to learn and follow human ownership norms by developing a system that represents ownership as probabilistic relations and learns norms through incremental algorithms and Bayesian inference, demonstrating competence in object manipulation tasks in simulated and real-world experiments.
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.