Robot dossier
TIAGo Pro
Release
Jan 1, 2026
Price
Price TBA
Connectivity
5
Status
Active
Height
Not officially disclosed; includes a 35cm lifting torso
Payload
3kg per arm excluding end-effector
TIAGo Pro is PAL Robotics' next-generation open-source mobile manipulator for advanced research and applied development. The official product page lists a dual-arm configuration with two 7-DoF torque-sensed arms, 3 kg payload per arm, quick tool changers, PAL parallel grippers, a 35 cm lifting torso, an RGB-D expressive head, dual 360-degree LiDAR, an omnidirectional Mecanum base, and ROS 2 software integrations including MoveIt 2, Nav2, ros2_control, MuJoCo, and Gazebo. PAL showed live TIAGo Pro teleoperation at MWC Barcelona 2026 and plans further ICRA 2026 demonstrations around navigation, manipulation, perception, data collection, and embodied AI; public pricing, battery autonomy, robot weight, and final customer configurations remain quote-only or undisclosed.
Listed price
Price TBA
PAL Robotics lists TIAGo Pro through a get-your-robot/contact flow; no public list price has been disclosed.
Release window
Jan 1, 2026
Current status
Active
PAL Robotics
Last verified
May 13, 2026
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Technical overview
A fast read on the mechanical profile, sensing package, and platform integrations behind TIAGo Pro.
Height
Not officially disclosed; includes a 35cm lifting torso
Weight
Not officially disclosed
Dimensions
23 DoF total workspace; approximately 7m³ coverage; 92cm vertical arm reach; 236cm horizontal reach; 35cm torso lift
Battery Life
Not officially disclosed
Charging Time
Not officially disclosed
Max Speed
Not officially disclosed
Payload
3kg per arm excluding end-effector
Operational profile
Capabilities
12
Connectivity
5
Key capabilities
Ecosystem fit
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The TIAGo Pro is a Research robot built by PAL Robotics. TIAGo Pro is PAL Robotics' next-generation open-source mobile manipulator for advanced research and applied development. The official product page lists a dual-arm configuration with two 7-DoF torque-sensed arms, 3 kg payload per arm, quick tool changers, PAL parallel grippers, a 35 cm lifting torso, an RGB-D expressive head, dual 360-degree LiDAR, an omnidirectional Mecanum base, and ROS 2 software integrations including MoveIt 2, Nav2, ros2_control, MuJoCo, and Gazebo. PAL showed live TIAGo Pro teleoperation at MWC Barcelona 2026 and plans further ICRA 2026 demonstrations around navigation, manipulation, perception, data collection, and embodied AI; public pricing, battery autonomy, robot weight, and final customer configurations remain quote-only or undisclosed.
Pricing has not been publicly disclosed. See all PAL Robotics robots on the PAL Robotics page.
Detailed specifications for the TIAGo Pro
Dimensions
23 DoF total workspace; approximately 7m³ coverage; 92cm vertical arm reach; 236cm horizontal reach; 35cm torso liftThe overall dimensions of 23 DoF total workspace; approximately 7m³ coverage; 92cm vertical arm reach; 236cm horizontal reach; 35cm torso lift define the robot's physical footprint and determine what spaces it can navigate and what clearances it requires for operation.
Payload Capacity
3kg per arm excluding end-effectorA payload capacity of 3kg per arm excluding end-effector determines what the robot can carry or manipulate. This is a critical spec for practical applications where the robot needs to handle physical objects.
The TIAGo Pro uses ROS 2 development stack with MoveIt 2, Nav2, ros2_control, PAL Web GUI, Docker PAL SDK image, RViz plugins, MuJoCo and Gazebo simulation support, and platform hooks for perception, teleoperation, embodied AI, and data collection workflows 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 TIAGo Pro integrates 6 sensor types, forming the perceptual foundation that enables autonomous operation.
This sensor configuration enables the TIAGo Pro 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 TIAGo Pro offers 12 distinct capabilities, each contributing to the robot's practical utility.
These capabilities work together with the robot's 6 onboard sensor types and ROS 2 development stack with MoveIt 2, Nav2, ros2_control, PAL Web GUI, Docker PAL SDK image, RViz plugins, MuJoCo and Gazebo simulation support, and platform hooks for perception, teleoperation, embodied AI, and data collection workflows AI platform to deliver practical, real-world performance.
The TIAGo Pro integrates with the following platforms and ecosystems, extending its utility beyond standalone operation.
This ecosystem compatibility enables the TIAGo Pro to work as part of a broader automation setup rather than operating in isolation.
12
Capabilities
6
Sensor Types
AI
ROS 2 development stack with…
Dual-arm manipulation gives the TIAGo Pro the ability to use both arms simultaneously and coordinately — a capability that mirrors human bimanual dexterity. This enables tasks that are difficult or impossible with a single arm: stabilizing an object with one hand while operating on it with the other, pouring from one container into another, or handling two independent tasks in parallel. The coordination between arms requires sophisticated motion planning that accounts for the physical constraints of both arms operating in the same workspace without collision while achieving the desired task outcome.
Autonomous navigation allows the TIAGo Pro to move through its environment without human guidance, planning efficient paths around obstacles and adapting to changes in real time. For a research robot, this involves simultaneous localization and mapping (SLAM) to build and maintain environmental models, path planning algorithms to find efficient routes, and reactive obstacle avoidance for unexpected situations. The complexity of autonomous navigation scales dramatically with the environment — navigating a structured warehouse is substantially different from navigating a cluttered home or outdoor space. The TIAGo Pro's navigation system must handle the specific challenges of its intended deployment scenarios reliably and repeatedly.
How the TIAGo Pro communicates with your network, smart home devices, cloud services, and companion apps.
The TIAGo Pro by PAL Robotics integrates 12 distinct technology components across sensing, connectivity, intelligence, and interaction layers.
The perception layer is built on RGB-D Head Camera, 4-Microphone Array, 2x 10m Laser Range Finder with 360° FoV, Torque Sensing on Each Arm Actuator, Optional 6-Axis Wrist Force/Torque Sensors, Optional Arm Camera Mount. 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 TIAGo Pro relies on Wi-Fi 6, Bluetooth 5.2, WireGuard VPN, EtherCAT 1kHz Arm/Torso Bus, Ethernet Expansion Ports. This connectivity stack ensures the robot can communicate with cloud services, local smart home devices, mobile apps, and other networked systems in its environment.
ROS 2 development stack with MoveIt 2, Nav2, ros2_control, PAL Web GUI, Docker PAL SDK image, RViz plugins, MuJoCo and Gazebo simulation support, and platform hooks for perception, teleoperation, embodied AI, and data collection workflows 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.
Pricing
The TIAGo Pro has a status of Active. Check with PAL Robotics for the latest availability details.
Engineering compromises and where this research robot excels
With 6 sensor types onboard, the TIAGo Pro 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.
Supporting 5 connectivity protocols gives the TIAGo Pro 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.
With 12 distinct capabilities, the TIAGo Pro 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.
PAL Robotics has not published a public price for the TIAGo Pro. While common for enterprise-class robotics, the absence of transparent pricing can complicate budgeting and comparison shopping. Prospective buyers will need to engage directly with the manufacturer for quotes, which may vary by configuration and volume.
Note: This strengths and trade-offs assessment is based on the TIAGo Pro'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 PAL Robotics 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 TIAGo Pro by PAL Robotics incorporates many of these technology pillars. For a detailed look at the specific sensors and components used in the TIAGo Pro, 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
PAL Robotics has not publicly disclosed pricing for the TIAGo Pro, which is typical for enterprise-focused robotics platforms that offer customized solutions and direct-sales relationships.
With 6 sensor types, the TIAGo Pro 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 TIAGo Pro 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 PAL Robotics's portfolio and market strategy, visit the PAL Robotics manufacturer page.
What the public profile tells you, and what still needs direct vendor confirmation
From a buying and rollout perspective, the TIAGo Pro should be read as a research platform aimed at labs and development teams validating robotics workflows. ui44 currently tracks 12 capability signals, 6 sensor inputs, and a last verification date of 2026-05-13. 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 PAL Robotics.
Commercial model
Pricing not public
PAL Robotics lists TIAGo Pro through a get-your-robot/contact flow; no public list price has been disclosed.. That usually means the final commercial package depends on deployment scope, services, or negotiated terms.
Integration posture
5 connectivity options
The profile lists Wi-Fi 6, Bluetooth 5.2, WireGuard VPN, EtherCAT 1kHz Arm/Torso Bus, Ethernet Expansion Ports, plus ROS 2 development stack with MoveIt 2, Nav2, ros2_control, PAL Web GUI, Docker PAL SDK image, RViz plugins, MuJoCo and Gazebo simulation support, and platform hooks for perception, teleoperation, embodied AI, and data collection workflows 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 8 declared compatibility links.
Spec disclosure
3/7 core specs public
ui44 currently has 3 of 7 core physical and operating specs filled in for this model, leaving 4 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 TIAGo Pro 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 PAL Robotics 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 PAL Robotics-specific support resources and documentation, visit the PAL Robotics page on ui44 or check the manufacturer's official website at PAL Robotics's product page.
All TIAGo Pro data on ui44 is verified against official PAL Robotics sources, including spec sheets, product pages, and press releases. Last verified: 2026-05-13. Official source: PAL Robotics product page. If you find outdated or incorrect information, please let us know — accuracy is our top priority.
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