# Improving Natural Language Interaction with Robots Using Advice

## TL;DR

This paper proposes a protocol for incorporating human advice into natural language understanding for robots, demonstrating that advice improves task performance and can be generated by the model itself to reduce effort.

## Contribution

It introduces a new protocol for including high-level advice in language-robot interaction, enhancing performance in grounded language understanding tasks.

## Key findings

- Advice significantly improves task success rates.
- Self-generated advice can still enhance performance.
- Simple advice leads to notable improvements.

## Abstract

Over the last few years, there has been growing interest in learning models for physically grounded language understanding tasks, such as the popular blocks world domain. These works typically view this problem as a single-step process, in which a human operator gives an instruction and an automated agent is evaluated on its ability to execute it. In this paper we take the first step towards increasing the bandwidth of this interaction, and suggest a protocol for including advice, high-level observations about the task, which can help constrain the agent's prediction. We evaluate our approach on the blocks world task, and show that even simple advice can help lead to significant performance improvements. To help reduce the effort involved in supplying the advice, we also explore model self-generated advice which can still improve results.