Commercial model
$250,000 list price
A published price gives buyers a starting point for budgeting, ROI modeling, and peer comparison before deeper vendor conversations begin.
Release
Jan 1, 2009
Price
€250.000
Connectivity
2
Status
Active
Height
104cm
Weight
22kg
iCub is an open-source humanoid robot designed for research into embodied cognition and artificial intelligence. Built by the Italian Institute of Technology (IIT) in Genoa, it's the size of a five-year-old child at 104 cm tall. Over 40 units are in use at research labs across Europe, the US, Korea, Singapore, China, and Japan. The hardware and software are fully open-source under GPL. It has 53 degrees of freedom, stereo vision cameras, microphones, and an optional full-body tactile skin. It can crawl, walk, sit, grasp objects, make facial expressions, and learn from interaction — making it one of the most capable research humanoids in the world.
Listed price
€250.000
Research platform: official IIT product catalog lists indicative non-profit build fees of €250,000 for a full iCub, €200,000 for the upper body, and €40,000 for the head; official quotation required.
Release window
Jan 1, 2009
Current status
Active
Italian Institute of Technology
Last verified
May 25, 2026
Share this robot
Open a plain share composer on X or Bluesky for this robot profile.
Technical overview
A fast read on the mechanical profile, sensing package, and platform integrations behind iCub.
Height
104cm
Weight
22kg
Battery Life
Battery backpack included on iCub 2.5/later full iCub versions; runtime not publicly disclosed
Max Speed
Not disclosed
Operational profile
Capabilities
9
Connectivity
2
Key capabilities
Ecosystem fit
Explore further
Benchmark set
Shortcuts to the closest alternatives in the current ui44 set.
Research
QRIO
Sony
Price TBA
Research
REEM-C
PAL Robotics
Price TBA
Research
ASIMO
Honda
Price TBA
Research
TALOS
PAL Robotics
Price TBA
The iCub is a Research robot built by Italian Institute of Technology. iCub is an open-source humanoid robot designed for research into embodied cognition and artificial intelligence. Built by the Italian Institute of Technology (IIT) in Genoa, it's the size of a five-year-old child at 104 cm tall. Over 40 units are in use at research labs across Europe, the US, Korea, Singapore, China, and Japan. The hardware and software are fully open-source under GPL. It has 53 degrees of freedom, stereo vision cameras, microphones, and an optional full-body tactile skin. It can crawl, walk, sit, grasp objects, make facial expressions, and learn from interaction — making it one of the most capable research humanoids in the world.
At a listed price of $250,000, it positions itself in the enterprise segment of the research market. See all Italian Institute of Technology robots on the Italian Institute of Technology page.
Detailed specifications for the iCub
Height
104cmAt 104cm, the iCub is sized for its intended operating environment and use cases.
Weight
22kgWeighing 22kg, the iCub balances structural integrity with portability and maneuverability.
Battery Life
Battery backpack included on iCub 2.5/later full iCub versions; runtime not publicly disclosedWith a battery life of Battery backpack included on iCub 2.5/later full iCub versions; runtime not publicly disclosed, the iCub can operate for sustained periods before requiring a recharge. Battery life is measured under typical operating conditions and may vary based on workload intensity and environmental factors.
AI Platform
YARP middleware + open-source ML frameworksThe iCub uses YARP middleware + open-source ML frameworks as its intelligence backbone. This AI platform powers the robot's decision-making, perception processing, and autonomous behavior. The sophistication of the AI stack directly impacts how well the robot handles unexpected situations and adapts to new environments.
The iCub integrates 7 sensor types, forming the perceptual foundation that enables autonomous operation.
This sensor configuration enables the iCub to perceive its environment and operate autonomously in its intended use cases. Multiple sensor modalities provide redundancy and more robust perception than any single sensor type alone.
Explore sensor technologies: components glossary · full components directory
Research robots serve as platforms for advancing robotics science and engineering. They enable researchers to test theories about locomotion, manipulation, perception, and human-robot interaction in controlled and real-world environments.
The iCub offers 9 distinct capabilities, each contributing to the robot's practical utility.
These capabilities work together with the robot's 7 onboard sensor types and YARP middleware + open-source ML frameworks AI platform to deliver practical, real-world performance.
The iCub integrates with the following platforms and ecosystems, extending its utility beyond standalone operation.
This ecosystem compatibility enables the iCub to work as part of a broader automation setup rather than operating in isolation.
9
Capabilities
7
Sensor Types
AI
YARP middleware + open-sourc…
How the iCub communicates with your network, smart home devices, cloud services, and companion apps.
The iCub by Italian Institute of Technology integrates 10 distinct technology components across sensing, connectivity, intelligence, and interaction layers. The physical platform features a height of 104cm, a weight of 22kg, providing the foundation on which this technology stack operates.
The perception layer is built on Stereo Cameras, Microphones, Hall-Effect Joint Sensors, Force/Torque Sensors, Tactile Skin (capacitive), Gyroscope, Accelerometer. These work in concert to give the robot a detailed understanding of its operating environment. This multi-sensor approach provides redundancy and enables the robot to function reliably even when individual sensors encounter challenging conditions such as low light, reflective surfaces, or cluttered spaces.
For communications, the iCub relies on Gigabit Ethernet, CANBus (internal). This connectivity stack ensures the robot can communicate with cloud services, local smart home devices, mobile apps, and other networked systems in its environment.
YARP middleware + open-source ML frameworks serves as the computational brain, processing sensor data, making navigation decisions, and orchestrating the robot's autonomous behaviors. The quality of this AI platform directly influences how well the robot handles novel situations, adapts to changes in its environment, and improves its performance over time through learning.
Research robots are acquired by universities, government labs, and corporate R&D departments. They serve as experimental platforms for developing new algorithms, testing locomotion strategies, and advancing the field of robotics. Some are also used for educational purposes.
Open-source software compatibility (ROS/ROS 2), sensor modularity, programmability, available SDK/API quality, community support, and published research papers using the platform are key factors. Documentation quality and the ability to modify both hardware and software are essential for research use.
Price Context
The iCub is in active commercial production and currently sold by Italian Institute of Technology. Check the manufacturer's website or authorized retailers for the latest stock and ordering information.
Engineering compromises and where this research robot excels
With 7 sensor types onboard, the iCub has one of the more comprehensive perception systems in the research category. This multi-modal approach enables robust environmental awareness, redundant obstacle detection, and reliable autonomous operation even in challenging conditions. More sensor diversity generally translates to better real-world adaptability.
With 9 distinct capabilities, the iCub is designed as a versatile platform rather than a single-task device. This breadth means the robot can handle varied scenarios and workflows, reducing the need for multiple specialized robots and increasing its utility across different situations.
At $250,000, the iCub represents a significant investment. While the price reflects the advanced technology and engineering involved, it places the robot firmly in the professional or enterprise segment. Buyers should build a thorough ROI analysis and consider the total cost of ownership, including integration, training, and ongoing maintenance.
Note: This strengths and trade-offs assessment is based on the iCub's documented specifications as tracked in the ui44 database. Real-world performance depends on deployment conditions, firmware maturity, and environmental factors. For the most current information, check the Italian Institute of Technology manufacturer page or visit the official product page. Use the comparison tool to evaluate these trade-offs against competing robots in the same category.
Understanding the engineering behind this category
Research robots serve a fundamentally different purpose than commercial or consumer models. They are platforms for discovery — enabling scientists and engineers to test theories, develop algorithms, and push the boundaries of what robots can do. The technology in research robots prioritizes openness, flexibility, and access to raw data over consumer-friendly packaging or commercial reliability. Understanding this distinction is important for anyone considering a research robot platform.
Research robots typically expose their navigation systems at a much lower level than commercial products. Researchers can access raw sensor data, modify SLAM algorithms, implement custom path planners, and test novel navigation approaches. ROS (Robot Operating System) and ROS 2 compatibility is standard, providing a common framework for sharing navigation modules across the research community. This openness enables rapid iteration — a researcher can swap between different SLAM implementations, test new obstacle avoidance strategies, or develop entirely novel navigation paradigms without being locked into a vendor's proprietary stack.
Research robots serve as physical testbeds for AI algorithms that may eventually appear in commercial products years later. Reinforcement learning, imitation learning, few-shot task learning, and human-robot interaction studies all require robot platforms that can execute AI-generated commands in the physical world. The gap between simulation (where training is cheap and fast) and reality (where physics is unforgiving) makes physical robot platforms essential for validating AI approaches. Research robots must support rapid deployment of new AI models without extensive integration work.
Research platforms prioritize sensor modularity and data access. Standard mounting interfaces allow researchers to attach custom sensors alongside built-in ones. Raw sensor data streams (not just processed results) are accessible for developing novel perception algorithms. Precise time-stamping and synchronization across sensor streams enable accurate multi-modal fusion research. Many research robots include more sensors than strictly necessary for any single application, providing researchers with rich datasets for developing and testing new algorithms.
Research robots balance operational runtime with practical lab use. Sessions of one to four hours are typical, with quick charging between experiments. Some research setups use tethered power for long-running experiments where battery limitations would interrupt data collection. Power monitoring and logging capabilities help researchers understand the energy costs of different behaviors and algorithms — important for developing efficient approaches that will eventually run on battery-constrained commercial systems.
Research environments present unique safety challenges because robots are constantly being programmed with untested behaviors. Hardware safety limits (joint speed caps, force limits, emergency stops) must be robust regardless of software commands. Safety-rated monitored stop and speed monitoring ensure the robot cannot exceed safe operating parameters even when running experimental code. Collaborative operation standards apply when researchers work alongside the robot during experiments. Many labs implement layered safety with physical barriers for high-speed testing and open-area operation restricted to validated, lower-risk behaviors.
Research robot platforms are becoming more accessible and capable. Cloud robotics enables remote experiment execution and shared datasets. Digital twins and high-fidelity simulators reduce the need for physical hardware time while improving sim-to-real transfer. Standardized benchmarks and open datasets enable fair comparison of results across labs. The democratization of robotics research — through lower-cost platforms, open-source software, and cloud infrastructure — is expanding who can contribute to advancing the field.
The iCub by Italian Institute of Technology incorporates many of these technology pillars. For a detailed look at the specific sensors and components used in the iCub, see the sensor analysis and connectivity sections above, or browse the complete components glossary for explanations of every technology used across the robotics industry.
How this robot compares in the research landscape
With a price point of $250,000, the iCub is squarely in the enterprise/professional segment. This pricing typically includes integration support, commercial-grade warranties, and ongoing software updates.
With 7 sensor types, the iCub has an extensive sensor suite. This comprehensive sensing capability places it among the more perception-capable robots in the research category, enabling more robust autonomous operation in varied conditions.
Being currently available for purchase gives the iCub a practical advantage over competitors still in development or prototype stages. Buyers can evaluate the actual product rather than relying on spec-sheet promises that may change before release.
Side-by-side specs, capability overlap analysis, and key differentiators.
For the full picture of Italian Institute of Technology's portfolio and market strategy, visit the Italian Institute of Technology manufacturer page.
What the public profile tells you, and what still needs direct vendor confirmation
From a buying and rollout perspective, the iCub should be read as a research platform aimed at labs and development teams validating robotics workflows. ui44 currently tracks 9 capability signals, 7 sensor inputs, and a last verification date of 2026-05-25. That mix gives buyers a useful first-pass picture, but it is still only the public layer of due diligence, especially when procurement, uptime, and support commitments are decided directly with Italian Institute of Technology.
Commercial model
$250,000 list price
A published price gives buyers a starting point for budgeting, ROI modeling, and peer comparison before deeper vendor conversations begin.
Integration posture
2 connectivity options
The profile lists Gigabit Ethernet, CANBus (internal), plus YARP middleware + open-source ML frameworks as the AI stack. That is enough to infer the basic network posture, but buyers should still confirm APIs, fleet management, and workflow integration details. ui44 currently tracks 4 declared compatibility links.
Spec disclosure
2/7 core specs public
ui44 currently has 2 of 7 core physical and operating specs filled in for this model, leaving 5 gaps that matter for deployment planning. Missing runtime, charge, speed, or payload details can materially change staffing and site-readiness assumptions.
The current profile is useful for scouting, but it still leaves meaningful operational unknowns. If this robot is heading toward a pilot or purchase discussion, the next step should be a structured vendor Q&A that fills the remaining runtime, charging, payload, safety, or integration blanks before anyone builds ROI assumptions around it.
If you want a faster apples-to-apples read, compare the iCub against nearby alternatives in ui44's compare view, then cross-check the underlying AI, sensor, and subsystem terms in the components glossary. For manufacturer-level context, the Italian Institute of Technology profile helps anchor this robot inside the wider product lineup.
Practical guide from day one through years of ownership
Research robot setup combines hardware assembly with software environment configuration. Unpack and assemble the platform following the manufacturer's documentation. Install the development framework — typically ROS or ROS 2 — and verify sensor connectivity. Calibrate all sensors using the manufacturer's tools and procedures. Set up the simulation environment (Gazebo, Isaac Sim, or equivalent) alongside the physical platform for parallel development. Establish version control for your experiment code and configuration. Document the initial calibration values and system state as your baseline for future reference. Plan network and computing infrastructure to handle the data rates your sensors will generate.
Research robots need maintenance that preserves the precision required for valid experimental results. Regularly verify sensor calibration — drift in camera intrinsics or IMU biases can invalidate experiment data. Maintain clean workspace conditions to protect optical sensors. Document any hardware modifications or maintenance performed, as these can affect experimental reproducibility. Update software dependencies carefully, documenting versions used for each experiment. Joint and actuator wear in research robots that perform repetitive tasks should be monitored and factored into experimental design.
Research robot software updates require careful management to maintain experiment reproducibility. Document the exact software versions used for each experiment. Test updates in a separate environment before applying to your experiment platform. Contribute bug fixes and improvements back to the community when using open-source frameworks. Be aware that ROS and other framework updates may require code changes in your custom packages — budget time for integration testing after major framework updates.
Research robots often have longer productive lives than commercial products because they can be upgraded and repurposed. Extend your investment by maintaining clean mechanical and electrical systems, documenting all modifications for future lab members, and keeping spare parts for common wear items. When specific components become obsolete, community forums and lab networks can be valuable sources for replacements. Consider the platform's modularity when planning future research directions — a platform that can accept new sensors and actuators adapts to evolving research questions.
For Italian Institute of Technology-specific support resources and documentation, visit the Italian Institute of Technology page on ui44 or check the manufacturer's official website at Italian Institute of Technology's product page.
All iCub data on ui44 is verified against official Italian Institute of Technology sources, including spec sheets, product pages, and press releases. Last verified: 2026-05-25. Official source: Italian Institute of Technology product page. If you find outdated or incorrect information, please let us know — accuracy is our top priority.
See how the iCub stacks up — compare specs, browse the research category, or search the full database.