Robots are steadily becoming easier to interact with. For decades, programming a robot meant learning a proprietary interface or mastering complex code.
Now, thanks to advances in natural language processing (NLP) and voice control, operators can instruct machines using everyday speech.
This shift promises to make robots more accessible to non-technical users, expand their applications, and ultimately accelerate adoption across industries.
From command lines to conversation
Traditional robot programming relied on explicit, structured commands – often in a scripting language designed for engineers. This created a steep learning curve, and in many cases, programming time was a significant share of a robot’s total cost of deployment.
Recent breakthroughs in NLP have changed the equation. By allowing robots to parse human language, engineers can issue instructions like “Pick up the blue container and place it on the third shelf” rather than writing lines of code. Even more importantly, NLP can interpret variations in phrasing, enabling flexibility in communication.
Voice interfaces add another layer of convenience. In situations where operators have their hands full – in warehouses, construction sites, or healthcare – spoken instructions are faster and more natural than navigating a screen.
How NLP works in robotics
NLP combines several AI disciplines, including speech recognition, syntactic parsing, and semantic understanding. In robotics, the process typically follows three stages:
- Speech-to-text conversion – The robot’s microphone captures an operator’s voice, and speech recognition software transcribes it.
- Language understanding – NLP models parse the sentence, identify intent, and extract key parameters.
- Action mapping – The interpreted command is translated into executable robot instructions, often using a middleware layer that connects the NLP engine to the robot’s control system.
This pipeline allows robots to respond to requests that are more natural and less rigidly structured than traditional programming languages.
Where voice-controlled robots are making an impact
Manufacturing and logistics
In factories and warehouses, workers can direct robots without interrupting their workflow. For example, a logistics operator might tell a mobile robot, “Bring me pallet number 14 from aisle C,” while continuing another task.
Healthcare
Service robots in hospitals can respond to requests from staff or patients, such as delivering supplies or guiding visitors. Voice control reduces the need for physical interfaces, improving hygiene and accessibility.
Hospitality and retail
Robotic assistants in hotels or stores can greet customers, answer questions, or take orders in natural language. This not only improves customer engagement but also reduces the need for human staff in repetitive roles.
Home robotics
Smart home robots – from vacuum cleaners to personal assistants – are already equipped with voice controls through platforms like Amazon Alexa, Google Assistant, and Apple Siri.
Technical challenges and limitations
While NLP and voice control have made great strides, several challenges remain:
- Accuracy in noisy environments – Industrial settings can overwhelm microphones and speech recognition systems.
- Context understanding – Robots still struggle with ambiguous instructions unless given clear, specific prompts.
- Multilingual support – Deployments in global markets require language models trained for local languages, dialects, and accents.
- Security and privacy – Voice interfaces can be vulnerable to spoofing or accidental activation, requiring robust safeguards.
Emerging research and breakthroughs
The integration of large language models (LLMs) into robotics is pushing conversational abilities forward. By leveraging models similar to those powering advanced AI chatbots, robots can handle more complex interactions, infer context, and even ask clarifying questions.
Another area of active development is speech-to-action learning, where robots directly map spoken commands to physical actions using reinforcement learning. This could eliminate some of the intermediate programming layers, making interaction even faster.
Leading companies and research groups
Several companies and institutions are pioneering NLP and voice interfaces for robotics:
- Boston Dynamics – Integrating voice control into mobile robots for security and inspection roles.
- SoftBank Robotics – Pepper, their humanoid robot, has conversational capabilities for customer service.
- Amazon Robotics – Leveraging Alexa’s NLP infrastructure to guide warehouse robots.
- PAL Robotics – Providing voice-interactive humanoids for research and commercial environments.
- Toyota Research Institute – Developing voice-controlled assistive robots for elder care.
- Fetch Robotics – Mobile platforms that can take verbal commands for material handling tasks.
- OpenAI / Figure AI collaboration – Exploring LLM-powered conversational control for humanoid robots.
OpenAI and Figure AI: Voice meets embodied robotics
Among the most promising developments in conversational robotics is the emerging combination of OpenAI’s advanced language models with Figure AI’s humanoid platforms.
While large tech companies such as Apple (Siri), Google (Google Voice), and Microsoft (Cortana) introduced voice recognition into mainstream devices years ago, these systems have often struggled to accurately interpret speech for some users, especially in more nuanced or free-form conversations.
In contrast, OpenAI’s voice-enabled models – already deployed in ChatGPT’s mobile app – have shown significantly greater flexibility and accuracy in real-time dialogue. For certain users, including those with less conventional speech patterns, this has translated into a markedly better experience compared with earlier voice assistants.
When paired with Figure AI’s humanoid robot, the result could be a step change in practical, voice-driven robotics. Figure’s most recent viral demonstration, covered by Robotics & Automation News earlier this month, showed its Figure 02 robot autonomously performing laundry tasks – picking garments from a basket and loading them into a washing machine – using its Helix Vision-Language-Action model.
Although it did not yet operate the washer’s controls, the dexterity and autonomy displayed marked a tangible milestone in household robotics.
Integrating a conversational AI system capable of understanding complex, spoken instructions with a humanoid robot able to physically execute them could accelerate the arrival of all-purpose, voice-controlled household robots.
While widespread commercial availability is still some distance away, this pairing may represent one of the most direct routes toward robots that operate in ways familiar from science fiction: understanding, responding, and acting with human-like fluency.
The road ahead
As NLP becomes more sophisticated, conversational robots will transition from following direct commands to collaborating more like human teammates. In the near term, expect voice-controlled robots to be increasingly common in logistics, healthcare, and service industries, where speed, accessibility, and hands-free operation deliver immediate value.
In the long term, the vision is for robots that can truly understand nuance, context, and intent – engaging in back-and-forth dialogue to clarify instructions and even suggest improvements to a task. This human-like interaction could be the catalyst that makes robotics a universal tool, not just for engineers, but for everyone.
