Humanoid (company)
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| Humanoid | |
|---|---|
| General information | |
| Legal name | SKL Robotics Ltd |
| Trading name | Humanoid |
| Founded | May 2024 |
| Founder | Artem Sokolov |
| Headquarters | London, United Kingdom |
| Other offices | Boston (USA), Vancouver (Canada) |
| Industry | Robotics, Artificial intelligence |
| Products | Humanoid robots (HMND 01) |
| AI framework | KinetIQ |
| Key people | Artem Sokolov (CEO), Jarad Cannon (CTO), Sotirios Stasinopoulos (CPO), Thomas Shepherd (COO) |
| Employees | 200+ (as of early 2026) |
| Funding | $50 million (founder-led) |
| Pre-orders | 20,500+ |
| Website | thehumanoid.ai |
Humanoid (legally registered as SKL Robotics Ltd) is a London-based robotics and artificial intelligence company that designs, develops, and deploys modular humanoid robots for industrial automation, and that built what it describes as the United Kingdom's first humanoid robot designed for industrial use, the HMND 01.[1] Founded in May 2024 by Artem Sokolov, the company pairs the HMND 01 hardware with KinetIQ, a proprietary four-layer AI framework that runs a vision-language-action model for manipulation and orchestrates fleets of robots end to end.[24] As of early 2026, Humanoid employs more than 200 people, is backed by $50 million in founder-led capital, and reports over 20,500 pre-orders for the HMND 01.[2][3]
The HMND 01 is a modular platform offered in two configurations: the Alpha Wheeled, which uses an omnidirectional wheeled base, and the Alpha Bipedal, which features full bipedal locomotion. Both variants share a common upper body and are powered by KinetIQ, so manipulation skills learned on one variant transfer to the other.[1][24]
Humanoid's team has been recruited from companies including Apple, Tesla, Google, Boston Dynamics, Sanctuary AI, iRobot, Brain Corp, Ocado Technology, Dyson Robotics, Arrival, and NVIDIA.[2] The company has completed multiple proof-of-concept deployments with industrial partners including Schaeffler, Siemens, Ford, SAP, and Martur Fompak. It has announced a five-year strategic technology partnership with Schaeffler and demonstrated multi-robot voice-activated collaboration at NVIDIA GTC 2026.[3][17]
Humanoid was founded in May 2024 by Artem Sokolov, a Russian-born entrepreneur and investor. Sokolov previously served as CEO of SOKOLOV, a Russian jewelry company founded by his parents Alexey and Elena Sokolov in 1993. He took over the family business in 2014 at age 21 and grew it over the following decade to 68.1 billion rubles in gross merchandise volume, more than 1,000 stores across 260 cities, and 96% brand awareness among Russian women aged 18 to 54. In August 2025, at age 32, Sokolov sold 100% of SOKOLOV to private investor Anton Pak through Aspring Capital, in what was reported as one of the largest Russian retail transactions in recent years. Pak, founder of the K2 Group investment company, confirmed that SOKOLOV would continue operating under its existing management team led by CEO Nikolai Polyakov.[4][5]
Sokolov has described his motivation for founding Humanoid as deeply personal. Growing up watching his grandparents spend their entire working lives performing repetitive tasks in jewelry production, he became determined to build machines that could "free people from routine and repetitive tasks" and enable them to pursue "more creative and meaningful work." He holds an Executive Certificate in AI and Digital Business Excellence from the IMD business school in Switzerland and was named EY Entrepreneur of the Year in 2021.[6][7]
The company registered as SKL Robotics Ltd in the United Kingdom and established its global headquarters in London. Sokolov is also founder and general partner of SKL.vc, a venture capital firm. From the outset, he emphasized a practical, market-ready approach over pure research, choosing to target industrial applications in logistics and manufacturing rather than pursuing general-purpose consumer robotics from day one.[8]
In May 2025, Humanoid appointed Jarad Cannon as Chief Technology Officer. Cannon brought over 13 years of robotics and AI experience focused on delivering scalable products. He previously served as CTO at Brain Corp, where he played a key role in scaling the company from 5 to over 40,000 deployed robots and growing the engineering team from 30 to 250 employees. At Brain Corp, he orchestrated the market launch of 13 different robotic systems, primarily in commercial floor care and inventory analytics. Before Brain Corp, Cannon spent six years as a software engineer at iRobot, working on telepresence robots, defense robots, and advanced behaviors for Roomba products. Cannon joined alongside existing CTO Dmitrii Rudnitckii, who subsequently became VP of Robotic Platform and Applications.[9]
On September 18, 2025, Humanoid unveiled the HMND 01 Alpha Wheeled, described as the United Kingdom's first humanoid robot designed for industrial use. The wheeled variant was developed from initial concept to working prototype in seven months, a timeline the company attributed to its simulation-first development approach using NVIDIA Isaac Sim and Isaac Lab. By designing, testing, and training the robot extensively in simulation before building physical hardware, the engineering team compressed a development cycle that typically takes 18 to 24 months in the humanoid robotics industry.[10][11]
The Alpha Wheeled stands 220 cm (7 ft 3 in) tall, weighs 300 kg, and uses an omnidirectional wheeled mobile base for locomotion at speeds up to 2 m/s (7.2 km/h). It features 29 degrees of freedom (excluding end-effectors), a bimanual payload capacity of 15 kg, and approximately 4 hours of runtime per charge. At the unveiling, Sokolov framed the company's purpose simply: "Robots shouldn't replace people; they should support them."[11]
In October 2025, Humanoid completed its first major proof-of-concept deployment with Schaeffler at the Schaeffler facility in Erlangen, Germany. The trial used a pre-alpha HMND 01 robot to perform bin picking of metallic bearing rings in cluttered industrial conditions. The robot continuously picked bearing rings from bins using parallel grippers, transferred them to a buffer table for further processing, and moved between stations in a near-production environment. Humanoid constructed a physical replica of the workstation in its lab and used teleoperation with leader arms to gather initial training data, which was then used to refine a pre-trained VLA (vision-language-action) model. Both companies reported that the POC results "fully met established expectations." The trial also demonstrated that humanoid robots hold advantages over traditional robotic arms and cobots for this use case, including mobility for multi-machine operation, AI models that generalize across product variants, and autonomous correction capabilities for real-time manipulation errors.[12]
On December 2, 2025, Humanoid announced the HMND 01 Alpha Bipedal, which went from initial design to working prototype in five months. The bipedal variant attracted significant industry attention for achieving stable walking just 48 hours after final assembly, a milestone that typically requires weeks or months for comparable humanoid platforms. This rapid sim-to-real transfer was enabled by extensive simulation training: the engineering team generated more than 52.5 million seconds of reinforcement learning locomotion data (equivalent to roughly 19 months of continuous training) in only two days using NVIDIA Isaac Sim and Isaac Lab. According to the company, the robot took its first real-world steps after just 3.2 million seconds of simulated training, with minimal domain randomization required to handle external pushes of up to 350 newtons.[13][14]
The Alpha Bipedal stands 179 cm (5 ft 10 in) tall and weighs 90 kg. It shares the same 29-degree-of-freedom upper body as the wheeled variant, enabling direct skill transfer between platforms. As of early 2026, the bipedal variant serves primarily as an R&D platform for future service and household applications.[15]
The HMND 01 made its North American debut at the Consumer Electronics Show (CES) 2026 in Las Vegas, held from January 6 to 9, 2026. At the Humanoid booth, the Alpha Wheeled performed a live demonstration of autonomous bin picking, extracting metallic bearing rings from cluttered industrial bins in a simulated factory environment. The demonstration highlighted the robot's computer vision capabilities and manipulation dexterity under realistic operating conditions. The Alpha Bipedal was also displayed at the booth, showcasing the company's multi-configuration approach. At CES, Humanoid and Schaeffler formally signed their five-year strategic technology partnership.[16]
On January 13, 2026, Humanoid and Schaeffler Technologies AG announced a five-year strategic technology partnership. Under the agreement, Schaeffler became the preferred supplier of strain wave gear actuators for Humanoid's wheeled robot platforms. These actuators are used primarily in the upper body, shoulders, and arms of the HMND 01, and their large hollow shaft design enables complete internal cabling of the robot's extremities.[17]
The partnership also includes a purchase agreement for Humanoid robots. Schaeffler plans to integrate several hundred humanoid robots into its global production network over the next five years, with initial beta-stage deployments scheduled for 2026 and 2027, focusing on validating technical integration, operational performance, and compliance with Schaeffler's system, safety, IT, and security requirements. The deployment will follow Humanoid's Robots-as-a-Service (RaaS) model for the gamma phase, with capital expenditure purchase options available for later stages. Schaeffler will contribute manufacturing data through teleoperation and synthetic generation to help train AI models tailored to its specific operational needs.[17]
Klaus Rosenfeld, CEO of Schaeffler AG, said of the deal: "As a Motion Technology Company, we want to play a key role in the growing humanoid robotics market." Sokolov used the partnership to restate his core thesis on commercialization: "The future of humanoid robotics will not be defined by the most impressive demonstrations but by its large-scale scalability."[17][36]
In January 2026, Humanoid and Siemens completed a two-week proof-of-concept deployment at the Siemens Electronics Factory in Erlangen, Germany. The HMND 01 Alpha Wheeled autonomously executed a tote-to-conveyor destacking task within Siemens' logistics operations, removing totes from stacked storage, transporting them across the facility, and placing them onto a conveyor at a designated handover point for human workers.[18]
| Metric | Result |
|---|---|
| Throughput | 60 tote moves per hour |
| Tote sizes handled | 2 different sizes |
| Continuous autonomous operation | 30+ minutes per stretch |
| Daily uptime | 8+ hours |
| Pick-and-place success rate | >90% |
Following the trial, both companies indicated that a wider rollout deploying larger numbers of humanoid robots across Siemens facilities could follow, pending further capability demonstrations.[19]
Over a six-week period in early 2026, Humanoid deployed the HMND 01 Alpha Wheeled at the Ford Innovation Centre in Cologne, Germany. The trial tested the robot in two complex automotive manufacturing workflows: tote handling for kitting operations and dual-arm manipulation of large metal car body parts. The robot autonomously moved totes between workstations with 8 kg payloads.[20]
| Metric | Result |
|---|---|
| Pick-and-place throughput | 83 units per hour (target: 50) |
| Continuous uninterrupted operation | 1 hour (target: 30 minutes) |
| Fully autonomous reliability | 97% |
| On-site data collection for model training | 1 hour to generate high-performing autonomous model |
The Ford POC demonstrated the robot's ability to rapidly adapt to new environments with minimal on-site training data. The robot exceeded all target performance metrics, achieving 66% higher throughput than the 50-unit-per-hour goal and sustaining autonomous operation for twice the targeted duration. The dual-arm task involved manipulating thin sheet-metal car body parts, traditionally difficult for autonomous systems because of the parts' flexibility and sharp edges. Humanoid and Ford Cologne subsequently announced plans to explore bringing humanoid robots into real production environments.[20][21]
From January to February 2026, Humanoid completed a proof of concept with SAP and Martur Fompak, a Turkish automotive parts supplier, in a live production logistics environment. The trial represented a significant technical milestone: it was the first time the HMND 01 was controlled by an external enterprise system in a production setting. The robot received task instructions from the SAP AI agent through the SAP Joule agent layer, autonomously navigated to designated pallet locations, retrieved KLT (Kleinladungstrager) boxes, and delivered them to trolleys. The robot successfully handled three different tote types with an 8 kg dual-arm payload operating limit. SAP Extended Warehouse Management sent tasks to the robot over the internet and managed its actions remotely, without requiring a custom local control system. Following the POC, the partners announced plans for further on-site validation phases and exploration of more complex use cases.[22][23]
In early 2026, Humanoid publicly introduced KinetIQ, its proprietary AI framework for end-to-end orchestration of humanoid robot fleets. The framework was designed as a cross-timescale architecture comprising four cognitive layers, from fleet-level task allocation down to millisecond-level joint control. The public release of KinetIQ positioned Humanoid not only as a hardware manufacturer but also as an AI software platform company, competing with architectures such as Figure AI's Helix.[24]
At NVIDIA GTC in San Jose on March 20, 2026, Humanoid presented a live demonstration of voice-activated multi-robot collaboration powered by KinetIQ. Two wheeled HMND 01 robots with grippers operated in a simulated retail environment. Booth visitors issued voice commands requesting the robots to handle items such as water bottles and popcorn. The KinetIQ system interpreted the requests, allocated tasks between the two robots, coordinated physical handovers between them, and returned the robots to default positions while awaiting the next command. Real-time status displays showed task distribution and execution progress.[25]
Artem Sokolov is the founder and CEO of Humanoid. Born in Krasnoye-na-Volge, Kostroma Oblast, Russia, he comes from a third-generation jewelry-making family. He did not complete a university degree, instead taking over his parents' jewelry business at age 21 and building it into one of Russia's largest jewelry brands by sales volume.[4]
| Detail | Information |
|---|---|
| Full name | Artem Sokolov |
| Role | Founder and CEO |
| Nationality | Russian |
| Previous role | CEO, SOKOLOV (jewelry, 2014 to 2025) |
| Education | Executive Certificate in AI and Digital Business Excellence, IMD (Switzerland) |
| Notable achievement | Grew SOKOLOV jewelry to 68.1 billion rubles GMV, 1,000+ stores, 96% brand awareness |
| Awards | EY Entrepreneur of the Year (2021) |
| Venture capital | Founder and general partner, SKL.vc |
| Motivation | Childhood observations of grandparents' repetitive factory labor |
Sokolov's experience scaling a consumer products company with over 7,000 employees and complex manufacturing operations has informed his approach to Humanoid, particularly regarding production scaling, supply chain management, and market-ready product development. He has stated that his strategy prioritizes practical, commercially deployable solutions over research demonstrations, emphasizing building a company that can ship production robots within its first few years rather than spending years in the lab. Sokolov has described his long-term vision as positioning robots as indispensable personal hardware comparable to mobile phones.[6]
Humanoid has assembled a leadership team with deep experience across robotics, AI, consumer technology, and manufacturing operations. The following table summarizes the company's key executives and their backgrounds as of early 2026.
| Name | Title | Background |
|---|---|---|
| Artem Sokolov | CEO and Founder | Former CEO of SOKOLOV jewelry ($1B valuation); EY Entrepreneur of the Year 2021 |
| Jarad Cannon | Chief Technology Officer | Former CTO at Brain Corp (scaled from 5 to 40,000 deployed robots); 6 years at iRobot |
| Sotirios Stasinopoulos | Chief Product Officer | 18+ years in robotics/AI; former Product Director at UBTECH Robotics; Ph.D. from Tsinghua University |
| Alina Kolpakova | Chief Strategy Officer | 8+ years at SOKOLOV alongside Sokolov; MBA from IE Business School |
| Jochen Rudat | Chief Growth and Revenue Officer | 10+ years at Tesla under Elon Musk; helped scale European operations and Gigafactory development |
| Boris Yangel | Head of AI | 17+ years in ML/DL; former Head of AI at Nebius; expertise in autonomous vehicles, voice assistants, and LLM-based agents |
| Dmitrii Rudnitckii | VP, Robotic Platform and Applications | 25+ years in robotics, AI, and autonomous systems; previously at Arrival and Nokia |
| Thomas Shepherd | Chief Operations Officer | 15+ years in advanced manufacturing; began career at General Dynamics; led operational transformation at Arrival |
| Todd Lewis | Head of Systems Engineering | 35 years in product design; former VP Hardware at Agility Robotics; delivered first commercially available humanoid robot |
| Hessam Maleki | Head of Locomanipulation | 17 years in robotics; former Director of R&D Programs at Sanctuary AI leading Phoenix humanoid development |
| Devin Billings | Head of Core Platform | 22 years in electrical engineering; led prototype teams at Boston Dynamics since 2009; headed electrical design for electric Atlas |
| Giulio Cerruti | Head of Control | 10+ years in R&D; Ph.D. on five-finger robotic hands; former Platform Team Lead at Dyson Robotics Research |
| Adam Kelsall | Head of Product Management | 15 years in engineering and product management; led product development at Ocado Technology |
| Jason Kline | Head of Design | 20 years in product design; former Director of Design at Arrival managing multiple vehicle programs |
The team draws from a wide range of leading robotics and technology organizations, including Boston Dynamics, Tesla, Agility Robotics, Sanctuary AI, iRobot, Brain Corp, Ocado Technology, Dyson Robotics, Arrival, Apple, Google, and NVIDIA.[2][26]
The HMND 01 is Humanoid's modular humanoid robot platform, designed around a shared upper-body architecture that can be paired with different lower-body configurations. This modular approach allows customers to select the mobility solution best suited to their operational environment while maintaining compatibility in manipulation capabilities, sensor suites, and AI software across variants. Skills learned on one variant can transfer to the other with minimal retraining. Humanoid describes the HMND 01 as "a next-gen labour automation unit, providing highly efficient services across various use cases and industries."[27]
| Specification | Alpha Wheeled | Alpha Bipedal |
|---|---|---|
| Height | 220 cm (7 ft 3 in) | 179 cm (5 ft 10 in) |
| Weight | 300 kg (661 lb) | 90 kg (198 lb) |
| Locomotion | Omnidirectional wheeled base | Bipedal legs |
| Max speed | 2 m/s (7.2 km/h) | 1.5 m/s (5.4 km/h) |
| Degrees of freedom | 29 (excl. end-effectors) | 29 (excl. end-effectors) |
| Payload capacity (bimanual) | 15 kg (33 lb) | 15 kg (33 lb) |
| Average runtime | 4 hours | 3 hours |
| Vertical reach | Floor level to 2 m | Floor level to ~1.8 m |
| Shelf access depth | Up to 60 cm | Up to 60 cm |
| Processors | NVIDIA Jetson Thor | NVIDIA Jetson Orin AGX + Intel i9 |
| End-effectors | 12-DOF hand or 1-DOF gripper | 12-DOF hand or 1-DOF gripper |
| Battery | Swappable | Swappable |
| Development time | 7 months | 5 months |
| Unveiled | September 2025 | December 2025 |
| Target price | $50,000 to $70,000 | ~$120,000 |
| Primary role | Industrial POC deployments | R&D / future service applications |
Both variants use modular end-effectors that can be swapped between a 12-degree-of-freedom dexterous hand (five fingers) for complex manipulation tasks and a 1-degree-of-freedom parallel gripper for high-speed pick-and-place operations. For perception, the HMND 01 carries 360-degree RGB cameras with two depth sensors. A distinctive design feature is its use of interchangeable protective garments, which serve to minimize contamination risks, cushion potential contacts in shared workspaces, and provide customizable visual appearance.[11][27]
Humanoid follows a structured three-phase product development roadmap.
| Phase | Description | Timeline |
|---|---|---|
| Alpha | Prototype and proof-of-concept testing with commercial partners | 2025 to early 2026 |
| Beta | Pre-production validation, broader commercial pilot deployments | Q3 2026 (Wheeled); late 2026 (Bipedal) |
| Gamma | Scaled production and commercial deployment under RaaS model | 2027+ |
The Alpha phase has involved prototype robots (including a pre-alpha unit) being tested in real-world industrial environments at facilities belonging to Schaeffler, Siemens, Ford, and Martur Fompak. Alpha robots gather insights on which functions are market-ready, which need refinement, and what new capabilities are required.
The Beta phase is scheduled to begin with the commercial Beta Wheeled variant in Q3 2026, followed by the Bipedal Beta in late 2026. Beta variants will be more compact and closer to production-ready, incorporating lessons learned from the Alpha-stage proof-of-concept deployments. Schaeffler's initial deployments are planned to use beta-stage robots in 2026 and 2027.
The Gamma phase is when Humanoid plans to begin producing at scale. The Robots-as-a-Service model will be the primary deployment method during this phase, with capital expenditure purchase options also available.[28][29]
KinetIQ is Humanoid's proprietary AI framework for end-to-end orchestration of humanoid robot fleets across industrial, service, and home applications. It uses a cross-timescale architecture comprising four cognitive layers that operate simultaneously at different temporal resolutions, from fleet-level task allocation down to millisecond-level joint control. Each layer treats the layer below it as a set of tools, orchestrating them through prompting and tool use to achieve goals set by the layer above. The framework supports cross-embodiment capability, meaning a single AI model can control robots with different morphologies (wheeled and bipedal) and end-effector configurations. KinetIQ is built around a vision-language-action model, placing Humanoid among the companies pursuing learned, generalist control policies for embodied AI rather than hand-engineered behaviors.[24][30]
| Layer | Name | Timescale | Function |
|---|---|---|---|
| System 3 | Fleet Orchestrator | Seconds | Allocates tasks across the robot fleet; integrates bidirectionally with WMS and ERP systems |
| System 2 | Robot-Level Executive | Seconds to subminutes | Uses an omni-modal language model to decompose goals into executable sub-tasks; dynamically updates plans based on real-time visual context |
| System 1 | Vision-Language-Action (VLA) | Subseconds (5 to 10 Hz) | Commands target poses for body parts (hands, torso, pelvis); exposes picking, placing, and locomotion as callable skills |
| System 0 | Whole-Body Control | Milliseconds (50 Hz) | Achieves pose targets while maintaining dynamic stability; uses RL-trained whole-body control for both bipedal and wheeled robots |
System 3 (Fleet Orchestrator) is an agentic AI layer that treats each individual robot as a tool in its repertoire. It ingests task requests, expected outcomes, standard operating procedures (SOPs), real-time updates, and facility context, then dynamically allocates tasks to optimize fleet-wide operations and integrates with facility management systems including warehouse management systems (WMS) and enterprise resource planning (ERP) platforms to receive task requests, track progress, report completions, and handle exceptions.[30]
System 2 (Robot-Level Executive) uses an omni-modal language model to observe the environment through the robot's sensors and interpret high-level instructions from System 3. Rather than following fixed task sequences, it dynamically decomposes goals into sub-tasks and updates plans based on real-time visual context. It also monitors execution and evaluates whether the System 1 VLA model is making progress, and when encountering situations beyond its capability, System 2 can escalate to human operators for intervention.[31]
System 1 (Vision-Language-Action) is a vision-language-action neural network that commands target poses for subsets of the robot's body parts at a subsecond timescale. It exposes multiple capabilities (picking, placing, manipulating, locomoting) that System 2 can invoke as needed. The VLA issues new predictions at 5 to 10 Hz, with each prediction containing a chunk of higher-frequency actions executed at 30 to 50 Hz by System 0. KinetIQ uses a prefix conditioning technique so that asynchronously prepared action chunks do not contradict events that unfold in the real world while the next chunk is being computed.[30]
System 0 (Whole-Body Control) operates at 50 Hz and is responsible for achieving pose targets while continuously guaranteeing dynamic stability across all robot joints. It uses reinforcement-learning-trained whole-body control for both bipedal and wheeled robots, which is the key enabler of KinetIQ's cross-embodiment capability: training data collected on one embodiment benefits the entire fleet. According to Humanoid, each System 0 model is trained on roughly 15,000 hours of simulation experience.[30]
A central design principle of KinetIQ is that upper-body control is kept invariant to the locomotion embodiment. This means VLA-based manipulation policies transfer cleanly between the wheeled and bipedal variants. Data collected by a wheeled robot performing warehouse tasks can improve the performance of a bipedal robot in a retail or service environment, and vice versa. This cross-embodiment approach reduces per-variant training costs and accelerates capability development across the product line.[30]
Humanoid has established a collaboration with NVIDIA to accelerate its robotic capabilities. The partnership involves three key NVIDIA technologies.[32]
| NVIDIA Technology | Role in HMND 01 |
|---|---|
| NVIDIA Jetson Thor / Orin AGX | Edge computing processor for on-device execution of robotic foundation models |
| NVIDIA Isaac Sim | Simulation framework for creating digital twins, validating hardware designs, and training |
| NVIDIA Isaac Lab | Reinforcement learning framework for locomotion and manipulation training |
Humanoid was one of the first European companies to integrate NVIDIA Jetson Thor into a humanoid robot prototype. The company also integrates NVIDIA's Isaac GR00T N1.7 vision-language-action model to enhance autonomous decision-making. Using NVIDIA's AI infrastructure, Humanoid reports that VLA model post-training can be completed in a few hours, and a locomotion policy can be trained from scratch and deployed on a physical robot within 24 hours.[32]
Humanoid has completed multiple proof-of-concept trials with major industrial partners, establishing the HMND 01 as one of the few humanoid robots with documented real-world industrial deployment results.
| Partner | Location | Period | Duration | Robot | Focus area | Key result |
|---|---|---|---|---|---|---|
| Schaeffler | Erlangen, Germany | October 2025 | ~2 weeks | Pre-alpha | Bin picking (metallic bearing rings) | Results "fully met established expectations" |
| Siemens | Erlangen, Germany | January 2026 | 2 weeks | Alpha Wheeled | Logistics (tote-to-conveyor destacking) | 60 tote moves/hour, >90% pick success rate |
| Ford | Cologne, Germany | Early 2026 | 6 weeks | Alpha Wheeled | Automotive (tote handling, dual-arm body part manipulation) | 83 units/hour (target: 50), 97% autonomous reliability |
| SAP / Martur Fompak | Turkey | January to February 2026 | ~4 weeks | Alpha Wheeled | Automotive logistics (ERP-controlled picking) | First external enterprise system control of HMND 01 |
Humanoid's earliest documented POC used a pre-alpha HMND 01 to perform bin picking of metallic bearing rings at Schaeffler's plant in Erlangen, Germany. The trial demonstrated that humanoid robots hold clear advantages over traditional robotic arms and cobots for this use case, including mobility for multi-machine operation, AI models that generalize across ring types, and autonomous correction capabilities. Data was collected via teleoperation with leader arms and used to refine a pre-trained VLA model. Following the POC, both companies moved into planning for a second phase using the Alpha robot.[12]
In January 2026, Humanoid and Siemens completed a two-week proof-of-concept deployment at the Siemens Electronics Factory in Erlangen, Germany. The HMND 01 Alpha Wheeled autonomously executed a tote-to-conveyor destacking task, achieving 60 tote moves per hour with over 90% pick-and-place success rate. The robot handled two different tote sizes, sustained 30+ minutes of continuous autonomous operation per stretch, and maintained 8+ hours of daily uptime. Following the trial, both companies indicated that a wider rollout could follow, pending further capability demonstrations.[18][19]
Over a six-week period in early 2026, Humanoid deployed the HMND 01 Alpha Wheeled at the Ford Innovation Centre in Cologne, Germany. The trial tested the robot in two complex automotive manufacturing workflows: tote handling for kitting operations and dual-arm manipulation of large metal car body parts. The robot exceeded all target performance metrics, achieving 83 units per hour throughput (66% above the 50-unit target), sustaining one hour of continuous uninterrupted operation (twice the 30-minute target), and reaching 97% fully autonomous reliability. Notably, the robot required only one hour of on-site data collection to generate a high-performing autonomous model, demonstrating rapid adaptability to new environments.[20][21]
From January to February 2026, Humanoid completed a proof of concept with SAP and Martur Fompak in a live production logistics environment. The robot received task instructions from the SAP AI agent through the SAP Joule agent layer, autonomously navigated to designated pallet locations, retrieved KLT boxes, and delivered them to trolleys. SAP Extended Warehouse Management sent tasks to the robot over the internet and managed its actions remotely, without requiring a custom local control system. This marked the first time the HMND 01 operated under the control of an external enterprise system in a production setting, proving that humanoid fleets can function as a direct physical extension of an enterprise's digital infrastructure.[22][23]
Humanoid operates primarily on a Robots-as-a-Service model rather than selling robots outright, though one-off sales to large manufacturers are also offered. Under this model, enterprise customers lease HMND 01 units and pay recurring monthly fees, reducing the upfront capital expenditure required for deployment. The company claims this approach can deliver labour cost savings of up to 50% annually for customers. Sokolov has indicated a target unit price range of $50,000 to $70,000 for the wheeled variant and approximately $120,000 for the bipedal variant, translating to an effective operational cost of approximately $10 per hour, significantly below the average wage for equivalent manual labor in developed economies.[33][34]
Humanoid targets an initial addressable market of approximately 250 million workers across retail, e-commerce, third-party logistics, manufacturing, and automotive sectors. The company cites a Goldman Sachs projection that the humanoid robot market will reach $38 billion by 2035 with 1.4 million shipments, and could grow to $1 trillion by 2050.[2][11]
Humanoid has outlined a three-phase deployment strategy.
| Phase | Target year | Application domain | Addressable market |
|---|---|---|---|
| Phase 1 | 2027 | Physical tasks (manufacturing, warehousing, logistics) | 250 million workers |
| Phase 2 | 2029 | Service sector (elder care, hospitality) | 1.4 billion people over 60 |
| Phase 3 | 2031+ | Household applications | 3.5 billion households |
The Phase 1 focus on industrial applications reflects Sokolov's market-ready philosophy: establish the robot's reliability and commercial viability in controlled, repeatable industrial settings before expanding to the more complex and variable demands of service-sector and household environments. Sokolov has emphasized augmentation over replacement, stating: "We want to redefine the synergy of robotics and human potential. It's not another futuristic AI dream."[8][35]
Humanoid is backed by $50 million in founder-led capital. Unlike many robotics startups that rely heavily on venture capital, the company's initial funding comes primarily from Sokolov's personal investment (approximately $30 million during the early stages) following his exit from the SOKOLOV jewelry business. This founder-led capital structure gives Sokolov significant control over the company's direction and development priorities. As of mid-2025, Humanoid was reported to be preparing for a Series A funding round to fuel further growth.[33][35]
The company has disclosed that it spends approximately 1 to 1.5 million pounds annually on training and computing infrastructure, reflecting the significant computational costs associated with reinforcement learning simulation and VLA model training.[35]
As of early 2026, Humanoid reports the following commercial metrics.[3]
| Metric | Value |
|---|---|
| Pre-orders | 20,500+ |
| Completed proofs of concept | 6 |
| Active pilot programs | 3 |
| Strategic partnerships | Schaeffler (5-year), NVIDIA |
| Team size | 200+ employees |
| Office locations | London, Boston, Vancouver |
The 20,500+ pre-orders represent substantial early market demand. As of mid-2025, the company had targeted 5,000 non-binding and 1,000 binding orders, along with a pre-order agreement for up to 2,000 units from a single customer. The company has stated that it is "already fully booked for 2026 early-year POCs," indicating strong demand from potential enterprise customers for trial deployments.[28][35]
| Location | Role | Details |
|---|---|---|
| London, United Kingdom | Global headquarters | Fifth-floor office on Edgware Road (WeWork) |
| Boston, United States | Hardware development and North American operations | Hardware engineering focus |
| Vancouver, Canada | Engineering and R&D | Engineering and research |
The London headquarters serves as the company's primary base for business operations, strategy, and European customer engagement. The Boston office supports North American market development and hardware engineering. The Vancouver office focuses on engineering and research. The geographic distribution across three major technology hubs in the UK, US, and Canada provides access to talent pools in robotics, AI, and engineering.[2][35]
Humanoid faces several notable challenges as it scales from proof-of-concept deployments toward commercial production.
The global market for AI and robotics specialists is intensely competitive. Humanoid competes for the same talent pool as companies like Tesla, Google, NVIDIA, and well-funded startups including Figure AI and Apptronik. The company acknowledges this as a significant challenge and has focused on assembling a team from diverse robotics backgrounds.[35]
The humanoid robotics supply chain is still nascent. Unlike automotive or consumer electronics, there is no established infrastructure of suppliers producing components specifically for humanoid robots at scale. Humanoid's partnership with Schaeffler for strain wave gear actuators is one step toward addressing this, but building a full production supply chain remains a major undertaking.[35]
Running large AI models on-device introduces latency and compute constraints. Humanoid's integration of NVIDIA Jetson Thor aims to address this by enabling larger robotic foundation models to run at the edge, but achieving real-time performance with increasingly complex models remains an engineering challenge.[35]
As Humanoid plans to expand from industrial to service and eventually household environments, safety and regulatory compliance become progressively more complex. The company expects a regulatory framework for humanoid robots to emerge around 2028 to 2029, and is developing safety systems in anticipation of these requirements. Safety features on the HMND 01 include force limiting, collision detection, emergency stop, and a collaborative mode with reduced speed and force when human workers are nearby.[15][35]
The $50 million in founder-led capital is modest compared to competitors. Figure AI has raised over $1.5 billion, Apptronik has raised $935 million, and Tesla can leverage its existing manufacturing scale and corporate resources. The planned Series A round is intended to close this gap.[35]
Humanoid operates in a rapidly growing humanoid robot market that has attracted significant investment and attention from major technology companies worldwide. The company's modular, industrial-first approach positions it within a competitive field of well-funded rivals.
| Company | Country | Robot | Locomotion | Funding | Key differentiator |
|---|---|---|---|---|---|
| Humanoid | UK | HMND 01 | Wheeled + bipedal | $50M (founder-led) | Modular platform, RaaS model, KinetIQ fleet orchestration |
| Tesla | USA | Optimus | Bipedal | Self-funded (corporate) | Manufacturing scale, $20K-$30K target price |
| Figure AI | USA | Figure 02 | Bipedal | $1.5B+ | Large funding, OpenAI/Microsoft/NVIDIA investors |
| Boston Dynamics | USA | Atlas | Bipedal | Hyundai subsidiary | Decades of locomotion research, 56 DOF |
| Agility Robotics | USA | Digit | Bipedal | $170M+ | Amazon partnership, warehouse specialization |
| Apptronik | USA | Apollo | Bipedal | $935M+ | Mercedes-Benz partnership |
| 1X Technologies | Norway/USA | NEO | Bipedal | $125M+ | Consumer/home focus, lightweight design |
| AgiBot | China | A2 | Bipedal | $500M+ | Largest shipment volume (10,000 units by Mar 2026) |
| Unitree | China | G1, H1 | Bipedal | Undisclosed | Low cost ($16K for G1), high volume |
Humanoid differentiates itself from competitors in several ways:
Humanoid faces notable competitive pressures: