In recent years, the development of large language models (LLMs) has attracted attention in the field of AI. These models, such as ChatGPT, LLaMA, and Bard, process text and image inputs and provide human-like responses for tasks requiring complex problem solving and advanced reasoning.
The next step in this technological advancement is robotics. By learning how to interact with the physical world, AI-based robots can improve repetitive tasks in a variety of fields, including logistics, transportation, manufacturing, retail, agriculture, and healthcare. This is expected to significantly improve efficiency in the physical world, as it is in the digital world.
The integration of AI and robotics is based on the following key elements:
1.
Base Model Approach: AI models like GPT are trained on diverse and extensive datasets, which means they require access to a large amount of diverse data.
2.
Large, high-quality datasets: For robots to learn success and failure based on real-world physical interactions, they need extensive, high-quality data.
3.
Role of Reinforcement Learning: Robot control and manipulation must move toward a goal that does not have a single correct answer. Deep reinforcement learning allows robots to automatically adapt their learning strategies and fine-tune their skills as they experience new scenarios.
These technological advances mean that a “GPT moment” in robotics is imminent. Robotic applications are already being implemented in real-world production environments, and commercially viable robotic applications are expected to grow exponentially by 2024.
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