Robots are able to have conversations with others by using a combination of technologies including speech recognition, natural language processing (NLP), and communication protocols. They convert spoken language into text, understand the meaning using advanced AI language models like GPT (Generative Pre-trained Transformer), then generate appropriate responses in text form, which can be vocalized through text-to-speech technologies. They also use sensors and algorithms to interpret social cues such as gaze, gestures, and tone, enabling them to manage conversational dynamics like turn-taking and addressing multiple participants effectively. In interactions between robots themselves, robots communicate wirelessly using a common language protocol to share events and feedback in real-time, allowing them to coordinate by asking questions, giving instructions, or waiting for certain actions, thus creating a kind of conversation to optimize joint tasks. For social robots conversing with humans, speech is recognized and transcribed (often via online speech recognition services), then processed by language models such as ChatGPT to generate responses. These robots can also express emotions and use body language for more natural, multimodal communication. Conversation management involves detecting speaker intentions, turn-taking, and moderating group dynamics for inclusive dialogue. Key components enabling robot conversation:
- Speech recognition to convert spoken words to text
- Large language models for understanding and generating text-based responses
- Text-to-speech to vocalize robot replies
- Wireless communication protocols for robot-to-robot interaction
- Sensors and algorithms for detecting social and conversational cues (gaze, tone, gestures)
- Conversation management frameworks to handle turn-taking and multi-party dialogue
These capabilities allow robots to engage in conversations, both with humans and with each other, adapting and coordinating dynamically as in human communication.
