Foundations of the Socio-physical Model of Activities (SOMA) for Autonomous Robotic Agents

TL;DR

This paper introduces the Socio-physical Model of Activities (SOMA), a framework combining physical and social contexts to improve autonomous agents' understanding and execution of everyday tasks, addressing challenges in natural language understanding and goal achievement.

Contribution

The paper proposes SOMA, a novel model integrating social and physical knowledge for autonomous agents, enabling better task understanding and generalization from prior experiences.

Findings

Validated SOMA through several use cases demonstrating its effectiveness.

Showed SOMA's ability to handle incomplete and ambiguous natural language commands.

Illustrated how SOMA facilitates generalization to new situations.

Abstract

In this paper, we present foundations of the Socio-physical Model of Activities (SOMA). SOMA represents both the physical as well as the social context of everyday activities. Such tasks seem to be trivial for humans, however, they pose severe problems for artificial agents. For starters, a natural language command requesting something will leave many pieces of information necessary for performing the task unspecified. Humans can solve such problems fast as we reduce the search space by recourse to prior knowledge such as a connected collection of plans that describe how certain goals can be achieved at various levels of abstraction. Rather than enumerating fine-grained physical contexts SOMA sets out to include socially constructed knowledge about the functions of actions to achieve a variety of goals or the roles objects can play in a given situation. As the human cognition system is capable of generalizing experiences into abstract knowledge pieces applicable to novel situations, we argue that both physical and social context need be modeled to tackle these challenges in a general manner. This is represented by the link between the physical and social context in SOMA where relationships are established between occurrences and generalizations of them, which has been demonstrated in several use cases that validate SOMA.