Discover how Xsens motion capture is transforming humanoid robotics R&D—enabling robots to learn from human movement, collaborate more intuitively, and bridge the gap between simulation and the real world.
Humanoid robotics is one of the most exciting and complex frontiers in today's world. Researchers are not only developing machines that walk and act like humans, but also ones that can learn, collaborate, and adapt in real time. Achieving this requires a deep understanding of human motion—and that’s where Xsens inertial motion capture technology makes a critical impact.
Below, we explore four core applications of Xsens in robotics R&D, each backed by real research and real-world projects.
Machine learning (ML) and artificial intelligence (AI) are transforming the way robots learn, plan, and interact with the world. One key enabler of this transformation is access to high-quality motion data—and that’s where Xsens comes in. This motion data is essential for training AI algorithms, reinforcement learning models, and neural networks in robotics
Xsens motion capture systems generate rich, time-stamped datasets that include joint angles, limb trajectories, body orientation, and more. These datasets can serve as valuable input for machine learning models that enable more adaptive, natural, and intelligent robot motion.
Because Xsens is wearable and markerless, researchers can gather training data in realistic environments, not just labs—leading to more robust and deployable models.
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When humans and robots collaborate physically—whether in manufacturing, medical, or service environments—it’s essential that the robot understands human motion with high fidelity and responsiveness. Motion capture plays a vital role in enabling these systems.
Researchers have used Xsens technology to develop adaptive control systems that allow robots to perceive human posture, anticipate motion, and respond safely. One such example is the Adaptive Collaborative Interface (ACI), developed by the Italian Institute of Technology to foster safer and more intuitive physical interaction between humans and robotic systems. Xsens' inertial sensors were used to stream full-body motion into this framework for collaborative tasks.
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In modern robotics, teaching robots by demonstration—also called imitation learning or learning from demonstration (LfD)—is a growing trend. Instead of programming every behavior manually, developers can use motion capture to show a robot how to act, allowing it to learn complex tasks from examples.
With Xsens, researchers can record full-body human movements and translate them into robot behaviors.
The Shanghai Humanoid Robots project demonstrated how humanoid robots could learn to move like humans by directly analyzing human motion data captured with Xsens sensors. This made it possible to generate smooth, human-like movements.
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Mapping human motion to robotic limbs is a common challenge in robotics R&D. Motion capture data is used here to accurately capture natural human movement and then resolve how that motion translates to a robot with different physical constraints or capabilities.
In a research study focused on mapping human arm motion to a redundant robot manipulator, IMU-based data (such as that from Xsens) helped derive joint configurations and end-effector paths. This type of mapping is foundational in teleoperation, robot skill learning, and motion retargeting.
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Xsens enables roboticists to go beyond simulation and code—to learn from real human movement, validate physical interaction, and create robots that act more naturally in the world. Whether it's for collaborative control, imitation learning, or motion mapping, motion capture is becoming an essential part of the roboticist’s toolkit.
Learn more about Xsens solutions for humanoid robot training.