# Towards Intention Recognition for Robotic Assistants Through Online POMDP Planning

arXiv: https://arxiv.org/abs/2411.17326 · HTML: https://mlsanity.com/paper/2411.17326/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes](https://mlsanity.com/paper/1302.1525/) (2013)
- [Active Goal Recognition](https://mlsanity.com/paper/1909.11173/) (2019)

## Built on by (most cited first, 1 of 1)

- [Uncertainty-Resilient Active Intention Recognition for Robotic Assistants](https://mlsanity.com/paper/2508.19150/) (2025)

## Abstract (arXiv)

Intention recognition, or the ability to anticipate the actions of another agent, plays a vital role in the design and development of automated assistants that can support humans in their daily tasks. In particular, industrial settings pose interesting challenges that include potential distractions for a decision-maker as well as noisy or incomplete observations. In such a setting, a robotic assistant tasked with helping and supporting a human worker must interleave information gathering actions with proactive tasks of its own, an approach that has been referred to as active goal recognition. In this paper we describe a partially observable model for online intention recognition, show some preliminary experimental results and discuss some of the challenges present in this family of problems.