# Enabling human-like task identification from natural conversation

arXiv: https://arxiv.org/abs/2008.10073 · HTML: https://mlsanity.com/paper/2008.10073/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Talk-to-Resolve: Combining scene understanding and spatial dialogue to resolve granular task ambiguity for a collocated robot](https://mlsanity.com/paper/2111.11099/) (2021)
- [DoRO: Disambiguation of referred object for embodied agents](https://mlsanity.com/paper/2207.14205/) (2022)
- [Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?](https://mlsanity.com/paper/2210.13189/) (2022)
- [tagE: Enabling an Embodied Agent to Understand Human Instructions](https://mlsanity.com/paper/2310.15605/) (2023)
- [Online Human Action Detection during Escorting](https://mlsanity.com/paper/2506.23573/) (2025)

## Abstract (arXiv)

A robot as a coworker or a cohabitant is becoming mainstream day-by-day with the development of low-cost sophisticated hardware. However, an accompanying software stack that can aid the usability of the robotic hardware remains the bottleneck of the process, especially if the robot is not dedicated to a single job. Programming a multi-purpose robot requires an on the fly mission scheduling capability that involves task identification and plan generation. The problem dimension increases if the robot accepts tasks from a human in natural language. Though recent advances in NLP and planner development can solve a variety of complex problems, their amalgamation for a dynamic robotic task handler is used in a limited scope. Specifically, the problem of formulating a planning problem from natural language instructions is not studied in details. In this work, we provide a non-trivial method to combine an NLP engine and a planner such that a robot can successfully identify tasks and all the relevant parameters and generate an accurate plan for the task. Additionally, some mechanism is required to resolve the ambiguity or missing pieces of information in natural language instruction. Thus, we also develop a dialogue strategy that aims to gather additional information with minimal question-answer iterations and only when it is necessary. This work makes a significant stride towards enabling a human-like task understanding capability in a robot.