# Towards Real-time Adaptation of Embodied Agent in Human-Robot Collaboration

arXiv: https://arxiv.org/abs/2412.00435 · HTML: https://mlsanity.com/paper/2412.00435/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 30 Nov 2024.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 11 of 11)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Embodied Question Answering](https://mlsanity.com/paper/1711.11543/) (2017)
- [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021)
- [PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks](https://mlsanity.com/paper/2411.00081/) (2024)
- [LLM-Powered Hierarchical Language Agent for Real-time Human-AI Coordination](https://mlsanity.com/paper/2312.15224/) (2023)
- [On the Importance of Environments in Human-Robot Coordination](https://mlsanity.com/paper/2106.10853/) (2021)
- [K-level Reasoning for Zero-Shot Coordination in Hanabi](https://mlsanity.com/paper/2207.07166/) (2022)
- [NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants](https://mlsanity.com/paper/2301.05223/) (2023)
- [AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy](https://mlsanity.com/paper/2402.07862/) (2024)
- [Algorithmic Scenario Generation as Quality Diversity Optimization](https://mlsanity.com/paper/2409.04711/) (2024)

## Abstract (arXiv)

Large Language Models (LLMs) have opened transformative possibilities for human-robot collaboration. However, enabling real-time collaboration requires both low latency and robust reasoning, and most LLMs suffer from high latency. To address this gap, we first propose a fine-grained benchmark that explicitly assesses agents' proactive adaptability and temporal responsiveness in the Overcooked-AI environment. Based on evaluation results, we propose MonTA (Monitor-then-Adapt), a hierarchical framework inspired by cognitive science research. MonTA contains three key modules: a lightweight Monitor that operates at high frequency (7 Hz) to detect adaptation needs, and two proficient Adapters for subtask and path adaptation reasoning that provide instructions to humans at a lower frequency. Our results demonstrate that MonTA significantly outperforms baseline agents on our proposed benchmark, achieving superior performance across layouts with varying teaming fluency. User studies confirm the high reasonableness of adaptation plans and consistent language instructions provided by our framework to humans.