---
language: en
license: mit
library_name: peft
base_model: llava-hf/llava-1.5-7b-hf
tags:
  - robotics
  - vision-language
  - task-detection
  - llava
datasets:
  - synthetic-data
---

# Model Card for Unsolvable Robotic Task Detection

## Model Details
- **Purpose:** Detects when robotic tasks are impossible to complete
- **Base Model:** LLaVA v1.5 7B
- **Developed by:** Duke University
- **Type:** Vision-Language Model

## Use Cases
- Identifying unsolvable robotic tasks in real-time
- Explaining why tasks cannot be completed
- Supporting safe human-robot interaction

## Training Data
- 4,920 synthetic images with question-answer pairs
- Covers five categories: Status Conflicts, Item Absences, Logical Contradictions, Ambiguous Tasks, and Ethical Constraints

## Performance
- Success rate on SDXL synthetic data: 78.05%
- Success rate on simulator synthetic data: 81.00%

## Limitations
- Works only with tasks similar to training data
- Requires human oversight
- May not catch novel types of impossible tasks

## Getting Started
```python
# Basic configuration
config = {
    "USE_LORA": True,
    "LORA_R": 8,
    "LORA_ALPHA": 8,
    "MODEL_MAX_LEN": 1024
}
```

## Contact
{yixuan.yang,yueqian.lin}@duke.edu