Intelligent Control in Embodied Robotics: Enhancing Human-Robot Interaction through Adaptive Control Techniques

Lingzhi Tian, Bofan Wu, Guoguang Wen

Keywords: Embodied Intelligence, Large Language Models (LLMs), Human-Robot Interaction, Adaptive Control, Data Amplification

TL;DR: This paper enables robots to adaptively adjust control methods for smarter human interactions.

Abstract:

Current embodied intelligence models often lack the ability to adjust control methods dynamically in response to human intentions, limiting their effectiveness in real-world interactions. This paper proposes a novel framework that enables robots to dynamically adapt their control parameters by integrating large language models (LLMs) with intelligent controllers.
Our approach simulates human-robot interactions and generates synthetic training data, allowing robots to better understand and respond to diverse human needs. We validate the framework using two commonly used control techniques and demonstrate that it can effectively adjust control methods, such as Proportional-Integral-Derivative (PID) and Nonlinear Model Predictive Control (NMPC), based on real-time human feedback. Experimental results show that our model enhances adaptability and responsiveness in human-robot interaction.
This work advances embodied intelligence by introducing an adaptive control framework and providing a scalable method for data generation, which together enable more intuitive and effective robot behaviors.

Primary Area: applications to robotics, autonomy, planning

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Submission Number: 10772