Robotics and autonomous systems
Moving from language-based interactions to physical world applications, we now explore how LLM-based agents are transforming robotics and autonomous systems. While traditional robots rely on pre-programmed behaviors and rigid control systems, agentic systems enable robots to understand natural language instructions, reason about their environment, and adapt their behavior dynamically. This integration of language models with physical control systems represents a fundamental shift in how robots interact with both humans and their environment.
Evolution of robotic agents
The marriage of LLMs with robotics has created systems that can bridge the gap between human intent and physical action. Unlike traditional robotic systems that operate on fixed rules, modern robotic agents can understand context, learn from experience, and make autonomous decisions while maintaining alignment with human objectives. Here are the key capabilities that distinguish...
