top of page

Intelligent Humanoid Robots: An Overview and Focus on Visual Perception Systems

In Dec 2023, Tesla unveiled a demo video of its Optimus Gen2 humanoid robot. With faster mobility, environmental recognition & memory, precise neural network vision, and dexterous dual-arm coordination for complex tasks, the robot showcased capabilities once thought reserved for science fiction.

Elon Musk boldly predicted that robotic demand will far outpace green vehicles in the years ahead. Many experts agree – 2024 could be the start of the "embodied AI" era, with the global humanoid robot market potentially ballooning to $34.43 billion by 2030 (The Insight Partners).


Tesla Optimus – Gen2 (Source: Optimus - Gen 2 by Tesla on YouTube)

Let’s explore this fascinating frontier today.

What Are Intelligent Humanoid Robots?

Artificial General Intelligence (AGI) requires emergent intelligence, autonomous agency, functional visibility, and physical embodiment. While today's large language models excel at the first two, they lack the latter qualities of environmental perception and physical instantiation. Intelligent robots are viewed as the most promising path to achieving those missing pieces of the AGI puzzle.

Intelligent humanoid robots are designed with human-like forms and functions – anthropomorphic limbs, mobility skills, sensory perception, learning, and cognition. They represent the pinnacle of complexity and control challenges among all types of robots. Humanoid robots are often used to assist or substitute humans in various tasks. With bipedal locomotion mirroring humans, these robots can seamlessly collaborate alongside people in real-world settings.

Why Design Them to Be Humanoid?

Our world's tools and infrastructure have been molded around human usage. By mimicking our form, humanoid robots can operate within existing environments without major overhauls. But their human-inspired design goes beyond just practical integration – the human body itself is an engineering marvel. Our physiques grant incredible flexibility, facilitating diverse mobility and adaptability across environments. Current imitation learning techniques allow humanoid robots to replicate our fluid movements and manipulation skills, rapidly mastering complex physical tasks.

Are Humanoid Robots Equivalent to Embodied AI?

Embodied AI refers to intelligent systems that perceive and act based on a physical body, interacting with the environment to acquire information, make decisions, and take actions, thus generating intelligent behaviors and adaptivity. Embodied AI does not have to be human-like. Humanoid robots are just one physical form of them.

The focus of embodied AI is capabilities like perception, cognition, learning, decision-making, and interaction with the environment. Humanoid robots refer to intelligent robots with human-like external features and motion abilities. They can walk on two legs, coordinate arms and body to function, and communicate with humans.

Components of Intelligent Humanoid Robots

Intelligent humanoid robots are engineered to emulate the human "perception-cognition-decision-execution" cycle. They contain mechanical body structures and actuated limbs akin to our musculoskeletal system. Sensor suites mimic our senses like vision, touch, and proprioception. Control architectures evoke the brain, cerebellum, and brain stem, governing cognition, decision-making, and motor control. Some even have interaction modules for communication akin to our social intelligence.


Intelligent Humanoid Robots Main Features

Ideally, intelligent humanoid robots resemble humans in form (anthropomorphic features, expressions), behaviors (motions, gestures), perception, and intellect (understanding, reasoning, speaking), and can interact with humans naturally.

Next, we’ll take a deeper look at their perception system.

Visual Perception Systems of Humanoid Robots

Humanoid robots rely on their perception systems – sensor arrays analogous to our senses – to gather information about their external environment and internal states. Common sensors include vision, touch/force, displacement & orientation sensors. Among these, vision sensors are paramount, as sight enables core reasoning about the world.

Vision System: The Eyes of Humanoid Robots

Just as over 80% of human knowledge is visually acquired, with half of the cerebral cortex involved in vision, advanced visual perception is vital for humanoid robots.


Visual Perception System, the Eyes of Intelligent Humanoid Robots

To operate in human environments, they must identify faces, detect objects, and ultimately understand their surroundings similar to humans. This starts from vision sensors like cameras and radar that provide optical images and depth data, to computer vision algorithms that process this raw sensory data into higher-level representations for intelligent planning, object recognition, navigation, pedestrian prediction, and more.

2D Vision vs. 3D Vision

Current leading humanoid robots typically integrate advanced machine vision systems, combining multimodal sensing with AI algorithms to enable perception, task execution, safety, and obstacle avoidance. These vision systems can be categorized into 2D and 3D varieties based on the image dimensions.

2D machine vision acquires 2D images and locates targets in x, y, rotation. It provides analysis based on contrast in grayscale or RGB images. However, it lacks 3D spatial information like height, surface orientation, volume, etc. 2D is also susceptible to lighting variations and struggles with moving objects. 3D machine vision provides richer target information, locating targets in x, y, z plus pitch, yaw, and roll. It reconstructs the 3D stereoscopic world of human eyesight. 2D and 3D excel in different scenarios, not in outright replacement.

Different Vision Solutions for Humanoid Robots

Given humanoid robots' lofty needs for wide field-of-view, high speed, and high accuracy perception in human environments, the technology approaches are still being actively pioneered. Different companies are taking diverse technical strategies. Popular humanoid robot vision solutions include Stereo Vision and ToF.

Stereo Vision uses two or more cameras to triangulate 3D scene information from multiple vantage points. For example, Tesla's Optimus robot employs an 8-camera vision system that combines algorithmic image processing for tasks like object detection and environment mapping. Time-of-flight (ToF) sensors like those used in Boston Dynamics' Atlas measure the time for light to travel to objects and reflect, enabling area scan imaging and depth perception without precision timing requirements. Notably, many current humanoid robots use multimodal fusion, combining cameras, LiDAR, etc. with infrared sensors, sonar, etc.

On the algorithm front, vision understanding leverages techniques like image segmentation, 3D reconstruction, and feature extraction. Meanwhile, visual navigation relies heavily on SLAM (simultaneous localization and mapping) combined with deep learning, or newer neural architectures like BEV (bird's eye view) transformers.

Case Study: Human-Model Collaboration Addresses Data Bottlenecks

Despite the technological innovations, data remains a key bottleneck constraining the advancement of robot visual intelligence algorithms. In this regard, autonomous vehicle companies have a distinct advantage through the scale of real-world driving data they've amassed.

Take Tesla for example. Its Optimus robot and electric cars use pure vision, reconstructing occluded static and dynamic objects as volumetric blobs using Occupancy Networks to identify navigable space and solve general obstacle detection.


Data Engine (Source: AI Dary 2022 by Tesla on YouTube)

We can see Tesla’s robots and cars share data sources, indeed. Car data and simulation data together constitute Tesla’s FSD data collection. Tesla unified modules for FSD and robots, enabling some algorithms to reuse – Optimus benefits from sizable existing FSD data.

Also, continuously adding new object categories to the dataset is needed to expand model perception capabilities. To efficiently filter massive data for quality training, Tesla employs human + model annotation, building a video training library for petabyte-scale raw driving footage. This library streams data directly for cloud training, further boosting neural network iteration efficiency. Such human-model collaborative data annotation has become a new paradigm, greatly reducing costs and accelerating deployment.

Outlook

Over the past two decades, we've witnessed robots gradually entering factory floors, logistics centers, and other controlled environments through technologies like collaborative robot arms and autonomous mobile robots.

As humanoid robots mature from low-volume specialized use cases to high production scales, their economics should improve dramatically driven by supply chain consolidation and volume efficiencies. In parallel, we can expect substantial capability advances from multimodal foundation models that unify skills like vision, language, audio, robotics, and more. Buoyed by these dual technological and economic forces, humanoid robots may soon proliferate into new real-world domains, their embodied AI abilities strengthening through tighter human-machine collaboration. While the path ahead remains uncharted, the quest for intelligent humanoid robots that can coexist seamlessly within our living, working, and social environments has begun in earnest.

Choose BasicAI as Your Data Partner to Accelerate Your Intelligent Humanoid Robot Project

If you're working on cutting-edge humanoid robot initiatives and need a trusted partner for visual data annotation, try BasicAI's secure data annotation platform and a global workforce of highly specialized human labelers. Our powerful data annotation tools and expertise span diverse use cases – from self-driving, industrial automation, medical imaging, and beyond. Let us help fuel your embodied AI vision!




 

Read next

Comments


BasicAI Cloud All-in-One Smart Data Annotation Platform

BasicAI Cloud

All-in-One
Smart
Data Annotation
Platform

Get a Quote for Your Data Labeling Project