## 3 System Hardware: The Pepper Humanoid Robot  
Pepper is a semi-humanoid robot developed by SoftBank Robotics, designed primarily for human-robot interaction and social engagement [12]. Standing at approximately 1.2 m tall and equipped with a tablet on its chest, Pepper is intended to communicate with users through both verbal and nonverbal cues, making it suitable for a variety of domains such as healthcare, education, and customer service.  
Unlike fully open source platforms, Pepper operates on a proprietary system called NAOqi OS, running on a Linux-based environment. This means that while the robot's hardware and lower-level firmware are not open source, developers can access a comprehensive set of Application Programming Interfaces (APIs) to extend Pepper's capabilities. SoftBank Robotics provides an official Software Development Kit (SDK) that primarily supports Python, though C++ and Java bindings are also available. The SDK includes modules for motion control, speech recognition, speech synthesis, face and person detection, and tactile sensor input, among other functionalities.  
To integrate new capabilities into Pepper, developers typically connect to the robot's onboard system over a local network, using NAOqi APIs to control its sensors, actuators, and cognitive modules. This enables the implementation of custom behaviors, allowing researchers to modify Pepper's interactive capacities without altering its physical hardware. In our project, we leveraged these existing APIs to incorporate advanced emotion recognition modules and enhanced natural language processing. For the multimodal emotion recognition, the audio input from Pepper's microphones, the visual data from its cameras, and textual interpretation from spoken inputs were processed externally, and the results were fed back into Pepper via the NAOqi APIs. This approach lets other researchers reproduce our work by following the following steps:  
1. Obtain a Pepper robot from SoftBank Robotics and ensure it is running the latest NAOqi OS version.
2. Install the official SDK and set up a development environment capable of connecting to [PERSON]'s IP address via the local network.
3. Implement or integrate external modules (vision, speech recognition, NLP, emotion detection) in standard Python environments. These modules can run on a local machine or on a dedicated server.
4. Use the NAOqi APIs to send commands to [PERSON]. For example, developers can use the ALMotion module to control [PERSON]'s movement, ALTextToSpeech for spoken output, ALAudioDevice for audio streaming, and ALVideoDevice for camera feed access.
5. Connect the emotion recognition and intention classification outputs from the external models back to [PERSON]'s behaviors. For instance, if the system detects the user is sad,Pepper can respond with a comforting remark using ALTextToSpeech, and adjust its posture to seem more empathetic via ALMotion.  
By relying on Pepper's official SDK and NAOqi APIs, our approach is fully reproducible. Other researchers with access to Pepper can replicate and extend our system by following standard guidelines and utilizing the provided code, ensuring that the scientific knowledge generated is both accessible and verifiable.