# Robot-Initiated Specification Repair through Grounded Language Interaction

arXiv: https://arxiv.org/abs/1710.01417 · HTML: https://mlsanity.com/paper/1710.01417/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement](https://mlsanity.com/paper/2210.00215/) (2022)
- [Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees](https://mlsanity.com/paper/2101.07140/) (2021)

## Abstract (arXiv)

Robots are required to execute increasingly complex instructions in dynamic environments, which can lead to a disconnect between the user's intent and the robot's representation of the instructions. In this paper we present a natural language instruction grounding framework which uses formal synthesis to enable the robot to identify necessary environment assumptions for the task to be successful. These assumptions are then expressed via natural language questions referencing objects in the environment. The user is prompted to confirm or reject the assumption. We demonstrate our approach on two tabletop pick-and-place tasks.