Aim is to bridges the gap between nlp and robotic control. By leveraging the power of Language Models (LLMs) within the Robot Operating System (ROS) framework, we hope this enables intuitive, natural language based control of robots.
Traditional robot control often requires specialized programming knowledge, limiting accessibility for non-experts. We want to addresses this challenge by interpreting natural language commands and translating them into precise robot actions. This approach significantly lowers the barrier to entry for robot operation, making it more accessible to a wider range of users.
- Natural language interface for robot control
- Integration of LLMs with ROS
- Flexible command interpretation
- Extensible to various robot platforms
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User Input: The user provides a natural language command (e.g., "Move forward for 2 meters then turn right").
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Language Processing: The LLM interprets the command, understanding the intent and extracting key parameters.
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Command Translation: The interpreted command is translated into ROS-compatible instructions.
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Robot Execution: The ROS system executes the translated commands, controlling the robot's movements.
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Feedback Loop: The robot's actions are monitored, and feedback is provided to the user.
- Educational robotics
- Assistive technologies
- Industrial automation
- R&D in human-robot interaction
- Integration with computer vision for enhanced environmental awareness
- Expansion to more complex robotic systems and tasks
- Development of multi-modal interaction caps. (voice, gesture, etc.)
This is currently in the conceptual and early dev. stage. We welcome contributions and collabs.