Real-time goal recognition using approximations in Euclidean space

TL;DR

This paper introduces a fast online goal recognition method suitable for both discrete and continuous domains, significantly reducing computational costs and enabling real-time robotics applications.

Contribution

It proposes a novel approach that minimizes planner calls or simplifies motion models, achieving sub-second recognition times for robotics.

Findings

Achieves orders of magnitude faster recognition than existing methods.

Effective in both discrete and continuous domains.

Enables real-time goal recognition for robotics applications.

Abstract

While recent work on online goal recognition efficiently infers goals under low observability, comparatively less work focuses on online goal recognition that works in both discrete and continuous domains. Online goal recognition approaches often rely on repeated calls to the planner at each new observation, incurring high computational costs. Recognizing goals online in continuous space quickly and reliably is critical for any trajectory planning problem since the real physical world is fast-moving, e.g. robot applications. We develop an efficient method for goal recognition that relies either on a single call to the planner for each possible goal in discrete domains or a simplified motion model that reduces the computational burden in continuous ones. The resulting approach performs the online component of recognition orders of magnitude faster than the current state of the art, making it the first online method effectively usable for robotics applications that require sub-second recognition.