metadata
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
# Basic configuration
config = {
"USE_LORA": True,
"LORA_R": 8,
"LORA_ALPHA": 8,
"MODEL_MAX_LEN": 1024
}
Contact
{yixuan.yang,yueqian.lin}@duke.edu