With the growing application of intelligent robots in service, manufacturing, and medical fields, efficient and natural interaction between humans and robots has become key to improving collaboration efficiency and user experience. Gesture recognition, as an intuitive and contactless interaction method, can overcome the limitations of traditional interfaces and enable real-time control and feedback of robot movements and behaviors. This study first reviews mainstream gesture recognition algorithms and their application on different sensing platforms (RGB cameras, depth cameras, and inertial measurement units). It then proposes a gesture recognition method based on multimodal feature fusion and a lightweight deep neural network that balances recognition accuracy with computational efficiency. At system level, a modular human-robot interaction architecture is constructed, comprising perception, decision, and execution layers, and gesture commands are transmitted and mapped to robot actions in real time via the ROS communication protocol. Through multiple comparative experiments on public gesture datasets and a self-collected dataset, the proposed method’s superiority is validated in terms of accuracy, response latency, and system robustness, while user-experience tests assess the interface’s usability. The results provide a reliable technical foundation for robot collaboration and service in complex scenarios, offering broad prospects for practical application and deployment.