Temel Kavramlar
Revolutionizing human-robot interaction through LLM-based systems.
Özet
The paper introduces a novel large language model (LLM) driven robotic system that enhances multi-modal human-robot interaction. Traditional systems required complex designs, but this new approach empowers researchers to regulate robot behavior through linguistic guidance, atomic actions, and examples. The system showcases proficiency in adapting to multi-modal inputs and dynamically interacting with humans through speech, facial expressions, and gestures.
Abstract:
- Presents an innovative LLM-driven robotic system for enhancing HRI.
- Empowers researchers to regulate robot behavior through linguistic guidance, atomic actions, and examples.
- Demonstrates proficiency in adapting to multi-modal inputs and dynamically interacting with humans.
Introduction:
- Seamless HRI requires adept handling of multi-modal input from humans.
- Traditional systems relied on intricate designs for intent estimation and behavior generation.
- New LLM-driven system shifts towards intuitive guidance-based approaches.
LLM Driven Human-Robot Interaction:
- System setup includes bi-manual robots with expressive capabilities.
- Architecture consists of "Scene Narrator," "Planner," and "Expresser" modules.
Evaluation Setup:
- Test scenario involves scripted interactions to test the robot's reasoning and expression capabilities.
- Preliminary results show successful assistance provision by the robot.
Conclusions and Future Work:
- LLMs have the potential to revolutionize robotic development.
- Future work includes comparing LLM-based interactions with rule-based approaches.
İstatistikler
This section illustrates the interaction flow within the system:
Felix said to Daniel: Can you pass me the fanta bottle?
Received 1 tool call(s).
Function(arguments='{}', name='get_objects ')
Following objects were observed: the_cola_bottle, the_fanta_bottle, glass_one, glass_two, etc.
...
You successfully finished the task.
Alıntılar
"The study proposes a novel LLM-based robotic system implemented on a physical robot."
"Our upcoming study will compare LLM-based interactions with rule-based approaches."