What Is a Humanoid Robot?

Robot learning is driven by adaptive algorithms and comprehensive training in both virtual and real-world settings. This lets humanoid robots acquire and refine intricate skills like bipedal locomotion, object manipulation, and social interactions. 

Developers use an optimized software stack that includes data ingestion and processing pipelines, training frameworks, and containerized microservices to power scalable and efficient training. AI foundation models, simulation environments, synthetic data, and specialized learning techniques such as reinforcement learning and imitation learning are used to train robots to perform tasks like grasping objects or navigating obstacles in different scenes.

Training uses digital twins that accurately simulate real scenarios, providing a risk-free environment for robot models to learn and improve. This eliminates the risk of physical damage and enables faster iteration by training many different models simultaneously. In simulations, operators can easily introduce variability and noise into scenes, giving robot models a richer set of experience data to learn from.

Once the robot’s skills are adequately refined in the digital world, the models can be deployed on the real robot. In some cases, training continues with the robot operating and practicing in the real world.    

Important emerging humanoid robot training techniques include: 

• Machine Learning: Humanoid robots are equipped with machine learning algorithms that let them analyze data to learn from past actions and process data from sensors to make informed decisions in real time.  
• Imitation Learning: Robots can acquire new skills by replicating movements demonstrated by humans. These actions are captured by sensors or cameras, then translated into robotic commands that mimic the observed behaviors. This approach is especially useful for teaching robots nuanced, complex tasks that are difficult to codify with traditional programming methods. 
• Reinforcement Learning: In this technique, an algorithm uses a mathematical equation to reward robots for correct actions and penalize them for incorrect actions. Through trial and error and the associated reward system, the robot adapts and improves its performance over time.

Designing humanoids to operate safely and collaborate with humans requires the integration of several safety concepts. 

• Traditional functional safety to ensure that robot hardware and software are designed to operate reliably and without causing harm.
• Robotics safety to enable emergency and protective stops to prevent accidents. 
• Human-machine collaborative safety to recognize and respond appropriately to human presence and actions.
• Sensor safety to test and validate the accuracy of robot sensors so humanoids can accurately perceive their environment.
• AI safety to check that the algorithms that humanoids rely on avoid misinterpretations and don't introduce new risks.

  Traditional functional safety and robotics safety are supported by existing safety standards such as IEC 61508, ISO 13849, and ANSI/RIA R15.606. There are several emerging standards such as ISO/IEC TS 22440 for AI safety and a new study group launched by the IEEE to explore and develop humanoid safety standards.

The goal of humanoid robots is to operate efficiently in environments that were built for humans. This will enable advancements in automation without the need to redesign spaces for robots. Humanoid robots have great potential to bring numerous benefits to warehouses and distribution centers, brick-and-mortar retail, clinical environments, and more.

• Human-Robot Interaction: Modeled after the human body, humanoid robots can work alongside people for collaboration on a variety of tasks.
• Versatility and Adaptability: Humanoid robots can take advantage of tools and infrastructure originally designed for human use.
• Productivity: Humanoid robots augment the completion of routine tasks by operating in the same environment as other workers.
• Enhanced Safety: Humanoid robots can undertake hazardous tasks and operate in places that are risky to human safety. This can reduce workplace accidents and reduce the need to expose humans to dangerous situations, including industrial settings and disaster response.

From manufacturing to healthcare, disaster response to customer service, humanoid robots can enhance human worker safety, productivity, and user experiences. While most humanoid use cases are in early R&D phases, there's tremendous excitement for their potential across industries to augment human workforces.

Manufacturing
Manufacturers can use humanoid robots in factories to support automation and work alongside their human workforce. With degrees of freedom (DOF) that resemble human flexibility, humanoids can handle parts to assist in assembly line tasks, perform repetitive or physically demanding work, and carry out inspection and maintenance tasks.

Warehouse and Logistics
Humanoid robots can perform tasks such as picking and packing products, receiving deliveries, managing inventory, and handling materials that require delicate manipulation. They can also collaborate with other robots such as robotic arms and autonomous mobile robots, as well as their human counterparts.

Healthcare
Humanoid robots are expected to complement human healthcare workers, potentially revolutionizing patient care, healthcare facility operations, and medical procedures.

Home Assistants
Humanoid robots can perform daily tasks like cleaning, cooking, and doing laundry. With natural language processing and machine learning, they can even communicate with humans and adapt to routines and preferences.

Customer Service
Humanoids can be employed in customer service roles in retail stores, airports, hotels, and other public spaces. They can provide information, guide customers to various locations, and handle basic inquiries—enhancing both operational efficiency and customer experiences.

Limited Training Data
Training AI models for effective humanoid robots requires an incredible amount of data. Precise human movements that humanoids are trained to mimic, along with the unpredictable nature of real-world scenarios, make it extremely challenging to gather and label the necessary data for optimal robot performance. Synthetically generated data can help address this challenge by creating artificial datasets that simulate various environments and movements.

Design
Humanoid robots must be lightweight, versatile, and powerful. Their control systems must also provide the balance and coordination needed for bipedal movement, while advanced sensors and actuators need to be precise and gentle enough to mimic the dexterity of the human hand. In addition, onboard computing systems must be small enough to fit on the humanoid form factor while having enough compute to run advanced AI models with low latency.

Energy Efficiency
Limited space for onboard batteries means that humanoids need to operate in a way that maximizes functionality while minimizing energy consumption. It’s crucial that autonomous robots can perform complex tasks without rapidly depleting their energy supplies.

Degrees of Freedom
Increased DOF allow humanoid robots to operate with greater dexterity and more human-like movements, letting them perform a wider range of tasks and adapt to various environments. However, this requires coordination across complex mechanical and control systems.

NVIDIA AI and Omniverse™ platforms accelerate humanoid robotics development. The three main computing platforms to get started with humanoid robotics include:

1. NVIDIA NeMo running on NVIDIA DGX™ supercomputers to train AI models for robotic use cases.
2. Robot learning and simulation frameworks such as NVIDIA Isaac™ Lab and NVIDIA Isaac Sim built on NVIDIA Omniverse that enable robots to learn and refine robot skills in simulated worlds.
3. NVIDIA Isaac on Jetson™ Thor, enabling humanoid robots to perform complex tasks and interact safely and naturally with both people and machines. Jetson Thor is based on the NVIDIA Blackwell architecture to run multimodal AI models for humanoid robots with an integrated functional safety processor, a high-performance CPU cluster, and 100Gb of ethernet bandwidth.