# Acquiring Grounded Representations of Words with Situated Interactive Instruction

arXiv: https://arxiv.org/abs/2502.20754 · HTML: https://mlsanity.com/paper/2502.20754/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 28 Feb 2025.

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

- [Using Natural Language for Human-Robot Collaboration in the Real World](https://mlsanity.com/paper/2508.11759/) (2025)
- [A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents](https://mlsanity.com/paper/2510.22052/) (2025)

## Abstract (arXiv)

We present an approach for acquiring grounded representations of words from mixed-initiative, situated interactions with a human instructor. The work focuses on the acquisition of diverse types of knowledge including perceptual, semantic, and procedural knowledge along with learning grounded meanings. Interactive learning allows the agent to control its learning by requesting instructions about unknown concepts, making learning efficient. Our approach has been instantiated in Soar and has been evaluated on a table-top robotic arm capable of manipulating small objects.