This approach allows the robot to efficiently perform multi-step navigation tasks using only text data, facilitating learning and improving performance in environments where visual data is scarce.
Unlike existing methods that require large amounts of visual data for training, the new approach can quickly generate synthetic data using large language models. This makes training data more accessible and helps bridge the gaps between simulation and the real world.
The use of text descriptions also simplifies the process of analyzing a robot’s performance, making it more understandable for humans and allowing them to quickly identify the reasons for unsuccessful attempts to achieve a goal.
While the new method does not completely replace traditional visual approaches, it offers a number of advantages, including versatility across a variety of tasks and environments and ease of understanding results through the use of natural language.
Source: Ferra
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