Core Concepts
MLLMs are enhanced with robotic-centric knowledge through ManipVQA, improving manipulation tasks.
Abstract
I. Abstract
- MLLMs integrated with robotic systems enhance natural language interpretation.
- Conventional MLLMs lack robotics knowledge, hindering manipulation tasks.
- ManipVQA bridges this gap by endowing MLLMs with manipulation-centric knowledge.
II. Introduction
- Large language models excel in vision-language alignment but face challenges in robotic applications.
- Robotic affordance and physical reasoning are crucial for effective manipulation tasks.
- Existing MLLMs lack specialized knowledge essential for robotics.
III. Methodology
A. Modeling of Affordances and Physical Concepts
- Understanding object affordances is vital for effective robot interaction.
- Physical concepts like transparency and liquid storage capacity are quantified for objects.
B. Instruction Dataset Construction
- Datasets like HANDAL and PhysObjects provide annotations for robotic needs.
C. Task Formulation
- REC and REG tasks are augmented with REC-Grounding-Affordance and REC-Physical tasks to enhance model capabilities.
D. MLLM Finetuning Strategy
- SPHINX framework is used with visual encoders to maintain general visual reasoning proficiency.
IV. Experiments
A. Implementation Details
- Fine-tuning conducted on NVIDIA GPUs using SPHINX framework.
B. Experimental Setup
- Evaluation on HANDAL dataset shows superior performance in object detection and affordance grounding.
C. Results
- Evaluation on PhysObjects dataset demonstrates improved physical concept grounding compared to GPT-4v.
D. Further Analysis
- Ablation studies show the importance of the ManipVQA dataset and visual ensembles in model performance.
V. Conclusion
ManipVQA enhances MLLMs with robotic-centric knowledge, improving their efficacy in manipulation tasks.
Stats
"Empirical evaluations conducted in robotic simulators demonstrate the robust performance of ManipVQA."
"Our research makes significant contributions to the fields of robotics and machine learning."