STAM: A Framework for Spatio-Temporal Affordance Maps

TL;DR

This paper introduces Spatio-Temporal Affordance Maps (STAM), a formalism that encodes environment action semantics to enhance autonomous robot task execution, addressing environment dynamism.

Contribution

The paper proposes a novel formalism for spatio-temporal affordances and maps, improving robot understanding of dynamic environments for better task performance.

Findings

Affordances encode accurate environment semantics.

STAM improves robot task execution in dynamic settings.

Experimental validation confirms effectiveness.

Abstract

Affordances have been introduced in literature as action opportunities that objects offer, and used in robotics to semantically represent their interconnection. However, when considering an environment instead of an object, the problem becomes more complex due to the dynamism of its state. To tackle this issue, we introduce the concept of Spatio-Temporal Affordances (STA) and Spatio-Temporal Affordance Map (STAM). Using this formalism, we encode action semantics related to the environment to improve task execution capabilities of an autonomous robot. We experimentally validate our approach to support the execution of robot tasks by showing that affordances encode accurate semantics of the environment.