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MoeBuTa committed May 27, 2024
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## Motivation

**Challenges with AI in Robotics for Traditional Approaches:**
- Limited Adaptability
- Struggle to adapt to new tasks or environments without reprogramming / retraining.
- High Dependence on Human Intervention
- Require significant human effort for data labeling and feature extraction for training models.
- Poor Generalization
- Lack the ability to generalize across different contexts and applications.
**Challenges with Traditional AI in Robotic systems:**
- Traditional AI models are great at specific, well-defined tasks but they face challenges when dealing with unpredictable, real-world environments.
- The variability of real-world tasks makes it hard for AI to generalize effectively.

**Proposed Research Topics**
- **Leveraging Large Language Models (LLMs) to control robots as an embodied agent**:
**Leveraging Large Language Models:**

```mermaid
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**Research Questions**
- How can LLMs be effectively integrated into general-purpose robotic systems to improve the
interpretation of natural language instructions and multi-modal sensory data for enhanced task planning and action
generation?
interpretation of natural language instructions and multi-modal sensory data for enhanced task planning and action generation?
- What are the optimal strategies that allows LLMs to access and utilize domain-specific knowledge in real-time to improve the performance and adaptability of general-purpose robots?
- How can we mitigate the risks of inaccurate or false information (hallucinations) generated by LLMs, such as mismatches between robots' actions and LLM-generated explanations, to enhance transparency and trust in human-robot interactions?

## Background

3 processes in robotic system
```mermaid
graph LR;
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## Current Work

LLM for Robotics Navigations using Eyesim simulator
LLM for mobile robot navigations using Eyesim


![img]({{ page.imgUrl }}eyesimllm.png)
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