Heuristic Online Goal Recognition in Continuous Domains

TL;DR

This paper introduces a new heuristic online goal recognition algorithm suitable for continuous domains, significantly improving efficiency and applicability in real-time robotic and navigational tasks.

Contribution

It presents a novel heuristic-based online goal recognition method that works in continuous spaces, with proven guarantees and extensive empirical evaluation.

Findings

Significant run-time improvements over existing methods

Effective in 3D navigation and robotic team scenarios

Proven theoretical guarantees for heuristics

Abstract

Goal recognition is the problem of inferring the goal of an agent, based on its observed actions. An inspiring approach - plan recognition by planning (PRP) - uses off-the-shelf planners to dynamically generate plans for given goals, eliminating the need for the traditional plan library. However, existing PRP formulation is inherently inefficient in online recognition, and cannot be used with motion planners for continuous spaces. In this paper, we utilize a different PRP formulation which allows for online goal recognition, and for application in continuous spaces. We present an online recognition algorithm, where two heuristic decision points may be used to improve run-time significantly over existing work. We specify heuristics for continuous domains, prove guarantees on their use, and empirically evaluate the algorithm over hundreds of experiments in both a 3D navigational environment and a cooperative robotic team task.