Registration: European Humanoid Robot Summit 2025
Abstract:OpenAI is intensifying its humanoid robot development by forming a team to develop control algorithms, integrating remote control with Nvidia Isaac simulation technology, and restarting its robot project to sprint towards AGI; despite having advantages in AI algorithm accumulation, it faces challenges such as fierce industry competition and difficulties in operating in unstructured environments.
OpenAI Intensifies Robot Development, Competing in the AGI Race
OpenAI is significantly increasing its investment in the robotics field, recruiting researchers focused on humanoid systems to explore new paths for advancing artificial intelligence (AI).
Recently, the company has recruited several researchers with expertise in AI algorithm development, which can be used to control humanoid robots and other types of robots. From the job descriptions, it is clear that OpenAI is forming a core team aimed at developing robotic systems that can be trained through remote control (Teleoperation) and simulation (Simulation) technologies.
Sources familiar with the company’s R&D dynamics revealed that OpenAI is also recruiting personnel specifically engaged in humanoid robot research (robots that possess partial or complete human forms). An industry insider deeply involved in cutting-edge robotics stated that OpenAI has initiated AI algorithm training work, which will enable a more precise understanding of the physical world, thereby assisting robots in environmental navigation and task execution.
1. OpenAI’s Robot Team Accelerates Expansion: Core Talent and Technical Direction
Recent recruitment activities indicate that OpenAI’s robot development process is accelerating comprehensively. For example, in June 2025, Chengshu Li joined OpenAI from Stanford University—during his time at Stanford, he participated in several robotics projects, including the development of a key benchmark designed to evaluate the performance of humanoid robots capable of performing various household tasks. Chengshu Li’s doctoral thesis focused on the construction of benchmark testing systems, with research subjects being “semi-humanoid robots” (these robots have two mechanical arms but use wheeled movement instead of legs).
Additionally, according to LinkedIn profiles, two other researchers from other robotics labs have also joined OpenAI. A professor from a university lab conducting humanoid robot research revealed that a student from that lab recently received a job offer from OpenAI.
OpenAI has not publicly responded to inquiries regarding its recruitment dynamics and robotics research plans. However, several job postings related to robotics on its official website have revealed clear technical directions. One position requires candidates to have expertise in “remote control and simulation technology”—these two technologies are core to training humanoid robots (including semi-humanoid and fully humanoid robots): when human operators remotely control the robot’s limbs to complete household tasks, AI algorithms simultaneously learn to “imitate human actions,” thereby achieving autonomous control capabilities. This position also explicitly requires familiarity with simulation tools such as Nvidia Isaac—Nvidia Isaac is currently the mainstream platform for training humanoid robots, with its core principle being to allow AI algorithms to complete training in a “virtual physical environment” before transferring to real-world scenarios.
It is still unclear whether OpenAI plans to “develop its own robot hardware,” “use off-the-shelf hardware,” or “collaborate with robotics companies.” However, another recently posted job for a “mechanical engineer” requires candidates to have “robotic system prototype development capabilities” and familiarity with “tactile and motion sensor integration”—experts in the robotics field analyze that this position suggests two possibilities: either OpenAI plans to develop its own robot hardware or is developing a remote control system for robot training. More notably, this position also requires “experience in designing mechanical systems for high-volume production (over 1 million units) and problem-solving capabilities on production lines”—this further indicates that the robotic systems developed by OpenAI may be aimed at “mass production” and could even be applied in manufacturing scenarios in the future.
All job descriptions related to robotics at OpenAI clearly state: “The core goal of the company’s robotics team is to unlock general-purpose robotics technology and promote AI’s evolution towards AGI levels in dynamic, real-world physical scenarios.”
2. The “Past and Present” of OpenAI’s Robot Development: From Early Breakthroughs to Restarting the Sprint
In fact, OpenAI has long been involved in the robotics field. At the company’s inception, it conducted influential robotics research—for example, in 2019, its developed AI algorithms successfully replicated the Rubik’s Cube solving process using humanoid robotic arms, demonstrating the potential of AI in precise physical operations.
However, in 2021, OpenAI paused its robotics development projects to focus resources on AI algorithm development, particularly large language models (LLMs)—this strategic adjustment directly led to the emergence of breakthrough products like ChatGPT. It wasn’t until 2024 that OpenAI restarted its robotics research; according to a report by The Information in December 2024, the company was then considering the possibility of “developing its own humanoid robots.”
Regarding the strategic logic behind OpenAI’s intensified robotics development, Stefanie Tellex, a robotics expert at Brown University, analyzed that “to develop more efficient robots, the core lies in designing and training AI models with ‘high frame rate, high-dimensional perception input processing capabilities’ and ‘high frame rate, high-dimensional physical output generation capabilities’—in other words, these models need to achieve ‘high fidelity perception (seeing the physical world) and action (executing physical operations).'” However, Tellex also stated that the specific technical details of OpenAI’s robotics development are currently unknown.
3. Humanoid Robot Technology: OpenAI’s Advantages and Challenges
(1) OpenAI’s Core Advantages: AI Algorithm Accumulation and AGI Strategic Synergy
As a leader in the global large language model field, OpenAI possesses two core advantages in robotics development:
1. Reuse of Cross-Modal AI Capabilities:
Currently, OpenAI has industry-leading models for “dialogue, reasoning, code generation, image and video generation”—these capabilities can be deeply integrated with robotics technology. For example, the “logical reasoning ability” of large language models can help robots understand complex task requirements (such as “organizing a desk and categorizing files into drawers”); image generation and video analysis models can enhance the robot’s “environmental perception accuracy,” allowing it to more accurately identify the location, material, and state of objects (such as distinguishing between “fragile glass cups” and “plastic bowls”).
2. Consistency of AGI Strategy:
OpenAI has always centered its core goal on “achieving AGI (general artificial intelligence that surpasses human intelligence),” and robotics is a key vehicle for AI “interacting with the physical world”—as the article analysis states, “OpenAI’s restart of robotics development essentially believes that the realization of AGI must rely on ‘AI algorithms that can interact with the physical world.'” This “algorithm-hardware-physical scene” collaborative layout provides a more “long-term strategic advantage” than companies that focus solely on robotics hardware development.
3. Integration Potential of Simulation and Remote Control Technologies:
From the recruitment demands, it is evident that OpenAI is focusing on breakthroughs in “simulation training” and “remote control” technologies—these two technologies are key to reducing training costs and improving training efficiency for humanoid robots. For example, through simulation platforms like Nvidia Isaac, AI algorithms can complete “millions of household training sessions” in a virtual environment without worrying about hardware wear; while remote control technology allows humans to “teach robots hands-on,” quickly accumulating high-quality training data—OpenAI’s algorithm accumulation in “reinforcement learning” and “imitation learning” will accelerate this process.
(2) Real Challenges Faced by OpenAI: Competition, Technical Bottlenecks, and Hardware Shortcomings
Despite significant advantages, OpenAI still faces three major challenges in the humanoid robotics field:
1. Intense Industry Competition:
The global humanoid robotics sector is currently characterized by a “multi-strong competition” pattern. On one hand, startups focusing on humanoid robots are rapidly emerging, such as Figure, Agility, and Apptronik, which have launched several prototypes by deeply engaging in hardware development and scene implementation; on the other hand, tech giants are also entering the fray, with Tesla’s Optimus robot and Google’s Robotics at Google team investing heavily in humanoid robotics. As Tellex stated, “In the humanoid robotics field, OpenAI does not have a ‘magical advantage’ and must compete alongside industry players.”
2. Technical Bottlenecks in Operating in Unstructured Environments:
Currently, humanoid robots can perform complex but “structured” actions like “dancing,” but they still have significant shortcomings in “unstructured environments” (such as homes, factories, and other dynamically changing scenarios)—for example, when faced with “a living room floor scattered with toys,” robots struggle to determine “how to avoid the toys and walk safely”; when confronted with “different shapes of bottle caps,” they find it difficult to adjust grip strength to achieve “damage-free opening.” Solving this issue requires AI algorithms to break through the “uncertainty modeling of the physical world”—this not only relies on data accumulation but also necessitates algorithmic innovation in the “perception-action-feedback” closed loop, for which no mature solutions have yet emerged in the industry.
3. Lack of Experience in Hardware Development:
From the recruitment of “mechanical engineers,” it appears that OpenAI may be attempting to develop its own robot hardware, but the company previously lacked experience in “large-scale hardware development and mass production.” Humanoid robot hardware involves multiple complex fields such as “high-precision motors, tactile sensors, and lightweight structural design”—for instance, a mechanical claw that can simulate the dexterity of human fingers needs to balance “strength, precision, and durability,” which entails long development cycles and high costs. In contrast, Tesla has hardware supply chain advantages from automotive manufacturing, while startups like Agility have years of hardware accumulation in robotics—OpenAI’s “catch-up” in hardware may take a considerable amount of time.
4. Current Status of the Humanoid Robot Industry: Technology Maturity and Market Potential
(1) Technology Maturity: Lower Hardware Barriers, Software Becomes the Core Competitive Point
In recent years, the “hardware development barriers” for humanoid robots have rapidly decreased: on one hand, the commercialization of core components such as new motors and sensors has improved, for example, the cost reduction of “high torque density motors” has made robotic limb movements more flexible; on the other hand, the proliferation of open-source hardware platforms and simulation tools (such as Nvidia Isaac, ROS robot operating system) allows companies to focus on “core algorithm optimization” without needing to develop hardware and basic software from scratch.
This trend of “hardware standardization and software differentiation” aligns perfectly with OpenAI’s “algorithm advantage”—as the article states, “the current competition in humanoid robotics has shifted from ‘hardware competition’ to ‘AI algorithm competition.'”
(2) Market Potential: Capital Enthusiasm and Long-Term Growth Expectations
Humanoid robots have become a “new hotspot” in the capital market: from early 2024 to now, venture capital firms have invested over $5 billion in humanoid robot startups; Morgan Stanley predicts that by 2050, the global humanoid robot industry will reach a scale of $5 trillion.
In terms of application scenarios, the long-term potential of humanoid robots is concentrated in three major areas:
1. Household Services:
Helping the elderly and disabled complete household chores and care tasks, alleviating labor shortages;
2. Manufacturing:
Replacing humans in “highly repetitive and dangerous” jobs (such as automotive assembly and heavy lifting);
3. Special Scenarios:
Such as disaster relief (entering dangerous areas inaccessible to humans) and space exploration (performing maintenance tasks on space stations).
However, in the short term, humanoid robots still face issues of “high costs and insufficient reliability”—for instance, the current mainstream humanoid robot prototypes cost hundreds of thousands of dollars, making widespread commercialization difficult; and they have high failure rates in complex scenarios, unable to meet the “24-hour continuous operation” industrial demand.
5. AGI and Robotics: An Indivisible Development Path
The article points out that OpenAI’s intensified robotics development is essentially a strategic judgment on the “AGI development path”—that is, “purely digital domain AI, such as language and images, cannot achieve true AGI; AGI must possess the ability to ‘understand the physical world and interact with it.'”
This judgment aligns closely with industry consensus: the core of human intelligence is not only “logical reasoning” but also the ability to “transform the physical world through bodily actions”—for example, humans can “adjust cooking heat based on the state of ingredients” and “adjust walking posture based on road conditions,” and these abilities rely on real-time closed loops of “perception-action-feedback.” For AI to achieve “human-like intelligence,” it must break through the boundaries of the “digital world” and enter the “physical world.”
Moreover, the “technical bottlenecks” that OpenAI has recently faced in the large language model field have also driven its expansion into robotics. As Tellex stated, “OpenAI has entered a ‘performance plateau’ on GPT-5, and to continue advancing AI, it must focus on the physical world.”—this means that robotics may become a new avenue for OpenAI to break through the “performance ceiling of large models.”
6. Conclusion: The “Short-Term Goals and Long-Term Significance” of OpenAI’s Robotics Development
In the short term, the core goal of OpenAI’s robotics development is to “build a triad of humanoid robot training systems based on ‘algorithms-simulation-remote control'” and may launch “prototypes” or “solutions in collaboration with hardware manufacturers” to validate the effectiveness of AI algorithms in physical scenarios.
In the long term, the significance of this layout lies in:
1) Building a “physical interaction carrier” for AGI, promoting AI’s evolution from “digital intelligence” to “general intelligence”;
2) Reshaping the “technological landscape” of the robotics industry—if OpenAI can deeply integrate large language models with robotic algorithms, it may give rise to a new generation of robots with “natural language interaction capabilities and autonomous task planning abilities,” fundamentally changing the production and lifestyle in fields such as household services and manufacturing;
3) Providing the industry with “algorithm standards”—just as ChatGPT redefined large language models, if OpenAI achieves breakthroughs in robotic algorithms, it may become a “technical benchmark” in the humanoid robotics field, promoting the standardized development of the entire industry.
However, the commercialization of humanoid robots still needs to overcome multiple barriers such as “cost, reliability, and scene adaptability”—whether OpenAI can win in this “AGI and robotics race” remains to be seen.
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