Release
Jan 1, 2017
Price
$10,900
Connectivity
4
Status
Available
Height
64 cm
Weight
5 kg
Battery
Depends on external battery pack
Speed
N/A (tabletop robot)
QTrobot
QTrobot is a tabletop social humanoid designed for human-robot interaction research, special-needs education, and therapy support. LuxAI positions it as a developer-friendly platform with ROS APIs and visual programming tools. IEEE Spectrum's Robots Guide lists QTrobot as a 2017 robot with a 64 cm height, 5 kg weight, and a €6,800 listed cost, while LuxAI's current documentation highlights integrated depth sensing, expressive gestures, and programmable behaviors for classroom and lab settings.
Listed price
$10,900
Official LuxAI shop pricing for QTrobot RD-V2 i5 (ex. VAT); also listed at $10,450 USD. Variants: RD-V2 i7 at €13,900, RD-V2 AI@Edge (Jetson Orin) also available.
Release window
Jan 1, 2017
Current status
Available
LuxAI
Last verified
Apr 7, 2026
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Technical overview
Core specifications and system stack
A fast read on the mechanical profile, sensing package, and platform integrations behind QTrobot.
Technical Specifications
Height
64 cm
Weight
5 kg
Dimensions
35 cm (W) × 21 cm (L) × 64 cm (H)
Battery Life
Depends on external battery pack
Charging Time
N/A (external power / battery pack)
Max Speed
N/A (tabletop robot)
Tech Components
Operational profile
How this robot is configured
Capabilities
9
Connectivity
4
Key capabilities
Ecosystem fit
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About the QTrobot
The QTrobot is a Research robot built by LuxAI. QTrobot is a tabletop social humanoid designed for human-robot interaction research, special-needs education, and therapy support. LuxAI positions it as a developer-friendly platform with ROS APIs and visual programming tools. IEEE Spectrum's Robots Guide lists QTrobot as a 2017 robot with a 64 cm height, 5 kg weight, and a €6,800 listed cost, while LuxAI's current documentation highlights integrated depth sensing, expressive gestures, and programmable behaviors for classroom and lab settings.
At a listed price of $10,900, it positions itself in the premium segment of the research market. See all LuxAI robots on the LuxAI page.
Spec Breakdown
Detailed specifications for the QTrobot
Height
64 cmAt 64 cm, the QTrobot is sized for its intended operating environment and use cases.
Weight
5 kgWeighing 5 kg, the QTrobot balances structural integrity with portability and maneuverability.
Dimensions
35 cm (W) × 21 cm (L) × 64 cm (H)The overall dimensions of 35 cm (W) × 21 cm (L) × 64 cm (H) define the robot's physical footprint and determine what spaces it can navigate and what clearances it requires for operation.
Battery Life
Depends on external battery packWith a battery life of Depends on external battery pack, the QTrobot 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.
Charging Time
N/A (external power / battery pack)A charging time of N/A (external power / battery pack) means the ratio of operation to downtime is an important consideration for applications requiring near-continuous availability. Some deployments use multiple robots in rotation to maintain uninterrupted service.
Maximum Speed
N/A (tabletop robot)A top speed of N/A (tabletop robot) is calibrated for the robot's primary operating environment and safety requirements.
The QTrobot uses ROS-based stack with Python/C++/Java APIs; RD-V2 variants include Intel NUC i5/i7 or NVIDIA Jetson AGX Orin options 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.
QTrobot Sensor Suite
The QTrobot integrates 3 sensor types, forming the perceptual foundation that enables autonomous operation.
This sensor configuration enables the QTrobot 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
QTrobot Use Cases & Applications
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.
Capabilities That Enable Real-World Use
The QTrobot offers 9 distinct capabilities, each contributing to the robot's practical utility.
These capabilities work together with the robot's 3 onboard sensor types and ROS-based stack with Python/C++/Java APIs; RD-V2 variants include Intel NUC i5/i7 or NVIDIA Jetson AGX Orin options AI platform to deliver practical, real-world performance.
Ecosystem Integration
The QTrobot integrates with the following platforms and ecosystems, extending its utility beyond standalone operation.
This ecosystem compatibility enables the QTrobot to work as part of a broader automation setup rather than operating in isolation.
QTrobot Capabilities
9
Capabilities
3
Sensor Types
AI
ROS-based stack with Python/…
Connectivity & Integration
How the QTrobot communicates with your network, smart home devices, cloud services, and companion apps.
Network & Communication Protocols
QTrobot Technology Stack Overview
The QTrobot by LuxAI integrates 8 distinct technology components across sensing, connectivity, intelligence, and interaction layers. The physical platform features a height of 64 cm, a weight of 5 kg, a top speed of N/A (tabletop robot), providing the foundation on which this technology stack operates.
Perception — 3 Sensor Types
The perception layer is built on Intel RealSense depth camera (D455 in LuxAI docs), ReSpeaker microphone array, Motor rotary encoder feedback (position, speed, overload, temperature). 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.
Connectivity — 4 Protocols
Intelligence — ROS-based stack with Python/C++/Java APIs; RD-V2 variants include Intel NUC i5/i7 or NVIDIA Jetson AGX Orin options
ROS-based stack with Python/C++/Java APIs; RD-V2 variants include Intel NUC i5/i7 or NVIDIA Jetson AGX Orin options 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.
Who Should Consider the QTrobot?
Target Audience
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.
Key Considerations
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
Availability
AvailableThe QTrobot is currently available for purchase. Check the manufacturer's website or authorized retailers for the latest stock and ordering information.
QTrobot: Strengths & Trade-offs
Engineering compromises and where this research robot excels
What the QTrobot does well
Versatile connectivity
Supporting 4 connectivity protocols gives the QTrobot flexible integration options. Whether connecting to local smart home networks, cloud services, or companion devices, the breadth of connectivity ensures compatibility across a wide range of deployment scenarios and reduces the risk of network-related limitations.
Broad capability set
With 9 distinct capabilities, the QTrobot 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.
Currently available
Unlike many robots that remain in development or prototype stages, the QTrobot is available for purchase today. This means you can evaluate the actual shipping product rather than making decisions based on projected specifications that may change before release.
Note: This strengths and trade-offs assessment is based on the QTrobot'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 LuxAI manufacturer page or visit the official product page. Use the comparison tool to evaluate these trade-offs against competing robots in the same category.
How Research Robot Technology Works
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.
Navigation & Mobility
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.
The Role of AI
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.
Sensor Fusion & Perception
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.
Power & Battery Management
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.
Safety by Design
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.
What's Next for Research Robots
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 QTrobot by LuxAI incorporates many of these technology pillars. For a detailed look at the specific sensors and components used in the QTrobot, see the sensor analysis and connectivity sections above, or browse the complete components glossary for explanations of every technology used across the robotics industry.
QTrobot in the Research Market
How this robot compares in the research landscape
With a price point of $10,900, the QTrobot is squarely in the enterprise/professional segment. This pricing typically includes integration support, commercial-grade warranties, and ongoing software updates.
The QTrobot's 3 sensor types provide solid perceptual coverage for its intended use cases. This mid-range sensor suite balances cost with capability, covering the essential modalities needed for research applications.
Being currently available for purchase gives the QTrobot 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.
Head-to-Head Comparisons
Side-by-side specs, capability overlap analysis, and key differentiators.
For the full picture of LuxAI's portfolio and market strategy, visit the LuxAI manufacturer page.
Owning the QTrobot: Setup, Maintenance & Tips
Practical guide from day one through years of ownership
Initial Setup
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.
Ongoing Maintenance
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.
Software Updates & Long-Term Support
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.
Maximizing Longevity
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 LuxAI-specific support resources and documentation, visit the LuxAI page on ui44 or check the manufacturer's official website at LuxAI's product page.
Frequently Asked Questions
What is the QTrobot?
How much does the QTrobot cost?
Is the QTrobot available to buy?
What sensors does the QTrobot have?
How long does the QTrobot battery last?
What AI does the QTrobot use?
How does the QTrobot compare to the Asimov DIY Kit (Here Be Dragons Edition)?
Does the QTrobot work with smart home systems?
How current is the QTrobot data on ui44?
Data Integrity
All QTrobot data on ui44 is verified against official LuxAI sources, including spec sheets, product pages, and press releases. Last verified: 2026-04-07. Official source: LuxAI product page. If you find outdated or incorrect information, please let us know — accuracy is our top priority.
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