Technical development and market maturity of humanoid robot systems in the USA

Market overview and the relevance of humanoid robotics

The US labor market faces a structural paradox: while the demand for physical labor in sectors such as logistics, manufacturing, and caregiving is increasing, the available labor supply for repetitive and physically demanding jobs is shrinking. Within this macroeconomic tension, the development of humanoid robots has evolved from a purely research-oriented topic into a capital-intensive race for market share. The relevance of comparing the leading US systems—Tesla (Optimus), Figure AI, Agility Robotics (Digit), Boston Dynamics (Atlas), Apptronik (Apollo), and Sanctuary AI (Phoenix)—stems not only from their differing technological approaches but also from their potential to automate existing infrastructures built for humans without costly modifications.

Unlike specialized industrial robots that operate in safety cages, these humanoid systems aim for direct coexistence with human workers. The years 2024 and 2025 marked a turning point: the integration of Vision-Language-Action Models (VLAs) and advanced neural networks expanded purely motor control to include a semantic dimension. Robots are no longer intended to simply follow programmed paths, but rather to understand instructions and react to unforeseen obstacles. However, the analysis of these six competitors reveals that the path to the universal "General Purpose Robot" is by no means uniform. Instead, radically different philosophies regarding actuators, cognition, and commercialization strategies are emerging.

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Technological convergence and the pursuit of universal intelligence

Despite the different technological DNA of the six companies, fundamental commonalities can be identified that define the current zeitgeist in robotics. All the systems examined are based on the premise of bipedal (two-legged) locomotion. This design paradigm is not chosen for aesthetic reasons, but out of the pragmatic necessity of moving in vertically organized environments – with stairs, narrow aisles, and shelves.

Another convergent element is the shift away from rigid rule sets towards data-driven learning approaches. Whether through teleoperation (remote control by humans to collect training data) or through simulation in virtual environments (such as NVIDIA Isaac Lab): all manufacturers are trying to solve the “Moravec Paradox”, which states that highly complex cognitive tasks are often easier for AI than the simple motor skills of a toddler.

Furthermore, all systems share similar energy constraints. Current battery technology typically limits autonomy to four to five hours under mixed load, making industrial shift work a logistical challenge. Solving this energy problem through fast charging or swapping systems is a key common focus. Ultimately, the overarching goal is identical: to create a system whose marginal costs tend to converge on electricity costs and which views labor as a “software application” that can be downloaded to hardware at will.

Systematic evaluation: architecture, cognition and physique

In order to objectively compare the six systems, a subdivision into key technical criteria is necessary: ​​actuators, manipulation, cognitive architecture and energy concept.

Regarding actuators, a clear trend towards electrification is emerging. While Boston Dynamics relied on hydraulics for years, the new Atlas marks the transition to all-electric drives that are quieter, cleaner, and more energy-efficient. Tesla, Apptronik, Figure, and Agility are also following this path. Sanctuary AI is an exception, continuing to use miniaturized hydraulics in its manipulators (hands) to achieve superior power density and speed, which electric motors often cannot yet offer in this installation space.

When it comes to manipulation (hands), there is a clear distinction between logistics specialists and generalists. Agility Robotics deliberately avoids five-jointed hands in its Digit robot in favor of gripping pads optimized for boxes (totes). This reduces complexity and costs. In contrast, Tesla, Figure, and Sanctuary are investing heavily in dexterous hands with up to 22 degrees of freedom (DoF) to emulate human fine motor skills.

The cognitive architecture is perhaps the strongest differentiating factor. Tesla relies on a radical end-to-end approach, where video data is directly translated into control commands, trained through its massive fleet of vehicles and teleoperation. Figure AI and Boston Dynamics, on the other hand, collaborate extensively with external AI partners (OpenAI and their own AI Institute, respectively, and NVIDIA) to integrate natural language understanding and planning. Sanctuary AI focuses heavily on data acquisition through teleoperation (“Carbon AI”) to teach the robot complex tasks through human demonstration.

Profile analysis: Tesla (Optimus)

Strengthen

Tesla's greatest advantage lies not in the robotics itself, but in its vertical integration and manufacturing depth. By accessing automotive technologies—from battery cells (4680) to inference chips (FSD computers)—Tesla can drive down costs more aggressively than any competitor. The Optimus robot directly benefits from advancements in "Full Self-Driving" software, particularly in visual navigation without LiDAR. Another unique selling point is the massive training volume: Tesla plans to deploy thousands of units in its own factories (Gigafactories), creating a closed data loop (data flywheel) that startups will struggle to replicate. The latest generation (Gen 3) hand-operated robot, with 22 degrees of freedom, aims to operate almost any tool a human would use.

Weaken

The biggest weakness lies in the discrepancy between announcements and reality. Timelines are regularly missed, and public demonstrations often take place under controlled conditions that don't reflect the unpredictability of a real factory environment. Furthermore, the system is a "walled garden": customers are entirely dependent on Tesla's software ecosystem. Critics also point out that the purely visual approach (without depth sensors like LiDAR) could reach its physical limits in low-contrast or poorly lit industrial environments, although Tesla denies this.

Profile analysis: Figure AI (Figure 02)

Strengthen

Figure AI has distinguished itself through its extremely fast iteration rate and high-profile partnerships. The collaboration with OpenAI enables the Figure 02 to perform speech processing and reasoning that is currently considered market-leading. The robot can "speak and listen" in real time, lowering the interaction barrier. The hardware design is extremely clean and optimized for mass production, with integrated wiring and an exoskeleton-like structure that promises robustness. Furthermore, the pilot project at BMW in Spartanburg demonstrates early validation in automotive manufacturing, one of the toughest testing grounds for accuracy.

Weaken

As a young company, Figure lacks the decades of mechatronics experience that competitors like Boston Dynamics possess. The long-term durability of the hardware under continuous load remains unproven. Furthermore, the reliance on external AI models (OpenAI) poses a strategic risk: changes to the API, costs, or availability of the AI ​​models could directly impact the functionality of the robot fleet. The balance between impressive demo performance and the harsh reality of 99.9% reliability in continuous operation has not yet been definitively achieved.

Profile analysis: Agility Robotics (Digit)

Strengthen

Agility Robotics pursues the most pragmatic approach in the field. The Digit is not a "wannabe human," but a specialized tool for material flow. Its legs, angled backward like a bird's, allow it to fold down when needed and reach shelf levels more efficiently. Through its partnership with Amazon and the establishment of the "RoboFab," Agility has gained a head start in commercialization and scaling. The focus on lifting and transporting standardized containers (totes) drastically reduces technical complexity, as no intricate manipulation is required. This makes the Digit arguably the most reliable system currently available for logistics applications.

Weaken

This specialization is also its greatest weakness. The Digit is hardly capable of performing tasks outside of logistics that require dexterity, such as assembly work or operating tools. Should the market move towards universal robots that can flexibly switch between warehousing and assembly, Agility's design could reach a dead end. Furthermore, due to its leg architecture, its movement often appears less intuitive to humans than that of anthropomorphic systems, which could affect social acceptance in mixed teams.

Profile analysis: Boston Dynamics (Atlas)

Strengthen

With the switch from hydraulics to electric drive in the new Atlas, Boston Dynamics has transformed its decades-long leadership in control theory into a commercially viable format. The new Atlas features joints with ranges of motion that surpass human physiology (e.g., 360-degree rotations of the torso and joints). This allows for movements that are more efficient than those of humans, as the robot doesn't need to "turn around" but simply changes its orientation. Integration into the Hyundai Group not only secures capital but also provides the perfect testing ground. The "Orbit" fleet management software has already been market-proven through the Spot robots.

Weaken

Despite its technical brilliance, Boston Dynamics has historically struggled to translate research projects into profitable products. Its late transition to an electric platform means it still lacks the long-term real-world data it has in customer environments, unlike Agility. Furthermore, the company needs to catch up quickly in the area of ​​high-level AI (semantic understanding), as its focus has long been primarily on low-level control (balance, dynamics). Partnerships with the Toyota Research Institute and NVIDIA are intended to close this gap, but the integration process is complex.

Profile analysis: Apptronik (Apollo)

Strengthen

Apptronik positions the Apollo as the “iPhone moment” of robotics, with a strong focus on safety and modularity. A key unique selling point is its interchangeable battery system. While other robots require hours of charging, Apollo can theoretically operate 24/7 with a manual battery swap. The design is explicitly intended to appear friendly and harmless, increasing acceptance in human-to-human interactions. Furthermore, the modularity allows the Apollo's upper body to be mounted on a stationary base, making it suitable for assembly line tasks where legs are not required. This reduces investment costs for customers.

Weaken

Compared to Tesla's resources or Figure AI's valuation, Apptronik is less financially aggressive. In a market where economies of scale determine price, this could be a disadvantage. The payload of approximately 25 kg is solid, but limited compared to specialized lifting devices. Positioning itself "between" pure logistics robots and high-end AI robots carries the risk of not becoming dominant in any niche ("stuck in the middle").

Profile Analysis: Sanctuary AI (Phoenix)

Strengthen

Sanctuary AI differentiates itself through its uncompromising focus on the human hand. The underlying premise is that a robot's intelligence manifests itself primarily in its ability to manipulate the world. By utilizing hydraulics in its hands, Phoenix achieves a dexterity and force control that is often superior to purely electric hands. The "Carbon AI" control system is designed to learn and progressively automate any conceivable task through teleoperation. This makes Phoenix potentially the most versatile robot for complex assembly tasks (e.g., sorting small parts, soldering, laboratory work) that its less dexterous competitors cannot perform.

Weaken

Hydraulics, even when miniaturized, carries the risk of leaks and requires more maintenance. Operating a hydraulic system cleanly in cleanroom or office environments is technically challenging. Furthermore, the teleoperation approach is extremely data-intensive. It is unclear how many millions of hours of human demonstration are needed to achieve full autonomy. As long as a human is required in the loop (pilot assistance), the business model is hardly viable, as the pilot's salary costs plus the robot's costs must be factored in. Betting on full autonomy through imitation is risky and time-consuming.

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Deployment scenarios: Specialization vs. Generalization

The choice of one of these systems depends largely on the specific “use case”.

For intralogistics and warehousing, where the primary focus is on transporting standard containers from point A to point B, Agility Robotics (Digit) is currently the most efficient choice. Integration with enterprise resource planning (ERP) systems is well-developed, and the system is specifically tailored to this purpose. Apptronik (Apollo) offers a strong alternative when shift work necessitates frequent battery swaps or when the robot is also required to perform stationary tasks on the assembly line.

In automotive manufacturing and complex assembly processes, where sheet metal needs to be held, screws inserted, or cables laid, Tesla (Optimus), Figure AI, and Boston Dynamics (Atlas) dominate. Here, the requirements for the overall body's mobility and integration into the digital factory are crucial. Tesla's price advantage could ultimately prove decisive, while Atlas can score points with its superhuman agility in confined spaces.

Sanctuary AI (Phoenix) is ideally suited for fine manipulation and laboratory tasks that require tactile feedback and dexterity. Scenarios involving the handling of objects with varying hardness, texture, or fragility (e.g., recycling sorting or textile processing) benefit from the hydraulic precision of hands.

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The path to scaling

Comparative analysis shows that the vision of a universal robot that makes coffee in the morning and welds cars in the afternoon is still a thing of the future. The current market is in a phase of divergence: Manufacturers are optimizing their systems for specific vertical markets (logistics vs. manufacturing) in order to generate a return on investment (ROI) for their customers as quickly as possible.

Technologically, the battle between hydraulic and electric actuators has largely been decided in favor of electrics, with the niche exception of high-performance hands. The real bottleneck is now shifting from hardware (which is increasingly "good enough") to data quality for AI. Here, Tesla, through its fleet strategy, and Figure/Sanctuary, through their simulation and teleoperation pipelines, have placed different bets.

For decision-makers, this means: There is no "best" robot. There is only the most suitable system for a defined process. Those who invest today are buying less of a finished product and more of access to a rapidly learning software platform. The coming years will see consolidation, in which those providers who survive are likely to be the ones who can't perform the most spectacular maneuvers, but who can handle the most mundane box transport most reliably and cost-effectively.

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