In this work, we focus on developing a framework for robot instructions based on natural language processing (NLP). We explore how to ground verbal commands in the physical world by leveraging NLP techniques and large language models like BERT and PaLM. Our approach enables robots to understand and execute complex tasks described in natural language, such as "pick up the red pen" or "move the vase to the living room."
To achieve this, we divide NLP into two directions: lexically-grounded methods and learning-based methods. Lexically-grounded methods involve using pre-defined dictionaries to associate words with specific actions, while learning-based methods use machine learning algorithms to learn these associations from data. We demonstrate the effectiveness of our approach by training a robot to perform single tasks, such as picking up objects or moving them to different locations.
Our proposed framework consists of three components: (1) natural language understanding, which involves using NLP techniques to interpret verbal commands; (2) task decomposition, which involves breaking down complex tasks into simpler sub-tasks; and (3) robot control, which involves executing the resulting sub-tasks using the robot’s end-effector poses.
We evaluate our approach through simulations and demonstrate its potential to enable robots to understand and execute complex natural language instructions. Our work has implications for improving the usability of robots in various applications, such as manufacturing, healthcare, and domestic service. By enabling robots to understand natural language instructions, we can make robots more useful and accessible to a wider range of users.
In summary, our article proposes a novel framework for robot instructions based on NLP techniques, which enables robots to understand and execute complex tasks described in natural language. Our approach leverages large language models like BERT and PaLM to ground verbal commands in the physical world, making robots more usable and accessible to a wider range of users.
Natural Language Processing for Robot Instructions: A Survey
