Kistler Group
Swiss measurement tech leader since 1959; piezoelectric force, torque and pressure sensors for robotics, automotive, and aerospace; 448M CHF revenue in 2024.
Kistler Group (legally Kistler Instrumente AG) is a privately held, family-owned Swiss measurement technology company headquartered at Eulachstrasse 22, 8408 Winterthur, Switzerland. The company was founded in 1959 by Hans Conrad Sonderegger, building on Walter P. Kistler's 1950 patent for the charge amplifier (charge-to-voltage converter), which enabled wide-scale industrial application of piezoelectric sensing technology. Today Kistler employs around 2,000 people at more than 60 locations worldwide and generated sales of 448 million Swiss francs in 2024, investing approximately 9% of annual revenue in R&D. The company holds around 700 active patents.
Kistler develops, manufactures, and distributes sensors for measuring dynamic pressure, force, torque, and acceleration, together with corresponding charge amplifiers, electronics, data acquisition systems, and analysis software. Key product families include piezoelectric force plates used in sports science and biomechanics (including at the 2024 Olympic Games), 6-axis force/torque sensors (such as the 9306A, measuring Fx/Fy up to ±5 kN, Fz up to 10 kN, and moments up to ±200 N·m), cavity pressure and process monitoring systems for injection molding (including the MaXYmos TL ML, the world's first FDA- and MDR-compliant process monitoring system for joining/assembly/testing processes), and the PiezoStar synthetic crystal line grown in-house using the Czochralski method since 1998.
Industries served include automotive (tire, NVH, crash testing, vehicle dynamics), aerospace (rocket engine testing, wind tunnel, landing gear), plastics and semiconductor manufacturing, medical devices, shipbuilding (large engine cylinder pressure monitoring), energy and infrastructure (bridge Weigh-in-Motion systems, structural health monitoring), and robotics. In robotics, Kistler 6-axis force/torque sensors are mounted at robot wrists to enable force-controlled assembly and collision detection.
The Kistler Group has grown through multiple acquisitions since the 1960s, absorbing companies such as Schatz AG (2016), Vester Elektronik GmbH (2017), eso GmbH (2017), AMS GmbH (2018), and LIK (2018). CEO Marc Schaad leads the group. Subsidiaries operate across Europe, North and South America, and Asia-Pacific. Contact: [email protected], +41 52 224 11 11.
Primary type & automation activities this supplier delivers:
MaXYmos TL ML
World's first FDA- and MDR-compliant process monitoring system for joining, assembly, and test procedures in medical device manufacturing.
Kistler Force and Torque Sensors
Piezoelectric force and torque sensors for dynamic measurement in robotics testing and industrial automation.
Contact Kistler Group
WEBSITE
https://www.kistler.com/enPHONE
+41 52 224 11 11HEADQUARTERS
Switzerland
Company Facts
Founded
1959
Primary Role
Component Supplier
Company Size
-
Primary Region
Europe
Annual Sales
-
Funding Stage
-
Funding Total
-
Keywords
- Kistler Group
- measurement technology
- piezoelectric
- force/torque sensor
- robotics sensor
- automotive testing
- crash test
- injection molding
- Industrial Manufacturing
- Core Component Supplier
- Motion & Navigation
- Component Supplier
- Manufacturer
- Perception & Vision
- End-effector & Grasping
- Manufacturing
- Healthcare & Senior Care
- Education & Research
- Automotive
EPFL and NYU Develop Scalable Robotic Fish for Diverse Aquatic Environments
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Chen Pu of Yuan Ke Vision Discusses Transitioning Robot Training Data from 2D to 4D with High Precision
On September 23, Chen Pu, Vice President of Product Development at Yuan Ke Vision, highlighted the importance of high-quality real data for robot training during a seminar in Beijing. He emphasized the need to elevate data from 2D to 4D and improve precision from centimeter to sub-millimeter levels to build a robust 4D data foundation for robotics. This transition is crucial as the industry faces challenges such as weak model generalization and inadequate scene adaptability. Chen noted that current training methods often rely on limited 2D video data, which fails to capture the complexities of three-dimensional space and temporal changes, hindering robots' ability to understand and interact with their environments effectively. Looking ahead, Yuan Ke Vision aims to address these challenges by developing new data collection and training paradigms that enhance dimensionality, enrich modalities, and improve precision. Chen pointed out that while video data serves as a foundation, it is often too simplistic, and the lack of tactile data remains a significant gap in the industry. No further timeline was disclosed at the time of publication.
Robot Training Data 4D Data Collection Industrial Robotics Embodied Intelligence Precision Robotics
Niantic Spatial Introduces Places Library Featuring 100 Real Environments for Robot Training
Niantic Spatial has launched its Places Library, providing robotics developers with a catalog of 100 real environments for training and evaluation. The assets, available as USDZ files, include two representations for each environment: a Gaussian splat for visual appearance and a mesh for collision detection. This initiative aims to enhance embodied AI training by offering realistic settings that align with gravity and support various simulators, including NVIDIA Isaac Sim. The significance of this launch lies in its potential to improve robot training efficiency. By utilizing real-world environments captured with a standard 360-degree camera, Niantic ensures that robots receive accurate visual and collision data, which is crucial for effective navigation and interaction. The library includes diverse settings, such as medical warehouses and urban streets, allowing developers to test their robots in varied scenarios without the need for extensive in-house data collection. Looking ahead, the Places Library could facilitate more advanced evaluations of robotic behavior in different environments. While the library's impact on performance across the 100 environments remains to be seen, it offers a valuable resource for robotics teams aiming to refine navigation tasks and adapt to changing surroundings. No further timeline was disclosed at the time of publication.
Niantic Spatial Flexion
Memo Unveils Physical-WAM to Enhance Embodied Intelligence at Global Digital Trade Expo
At the Global Digital Trade Expo on September 24, Memo introduced the Physical-WAM, the world's first physical-world action model equipped with physical perception capabilities. This innovation aims to address the challenges faced by robots in understanding and interacting with the physical world, which has hindered the widespread adoption of embodied intelligence. The significance of this development lies in its potential to bridge the data gap that currently limits robots' physical interactions. Memo's CEO, Li Minghao, highlighted that the lack of foundational physical understanding in robots stems from insufficient real-world interaction data and inadequate evaluation metrics. The Physical-WAM model is designed to enhance robots' predictive capabilities and real-time adjustments in physical tasks. Looking ahead, the demand for embodied intelligence is surging, with significant investments in the sector. However, challenges remain in aligning order fulfillment with funding growth. As the industry evolves, the effectiveness of solutions like Physical-WAM will be crucial in overcoming existing barriers to practical implementation.
Embodied Intelligence Physical AI Robotics Machine Learning
Robot Dogs Use Insect-Inspired Movements to See Through Obstructions in 17 Milliseconds
A collaborative team from Johannes Kepler University Linz, Free University of Bozen-Bolzano, University of Trento, and University of Sussex has developed a method for robot dogs to mimic insect movements to enhance their visual perception. By performing rhythmic lateral movements, akin to the 'peering' behavior observed in insects like locusts, these robots can effectively see through obstructions such as foliage and debris. This innovation is significant as it addresses a common challenge faced by exploration robots in environments cluttered with obstacles that hinder visual sensors. The research establishes a connection between the peering motion and synthetic aperture sensing, allowing the robots to create a virtual aperture that enhances background signals while blurring out obstructions. Future developments to watch include the potential applications of this technology in various environments, including forests and disaster sites. The research indicates that the synthetic aperture technique is most effective at around 50% obstruction density, suggesting a promising avenue for improving robotic vision in complex terrains. No further timeline was disclosed at the time of publication.
Robotics Computer Vision AI Obstacle Detection
RobotToday Weekly September 21 – 25, 2026
Cognex buys RealSense, Qualcomm acquires PickNik, the IFR counts 5 million factory robots, AGIBOT deploys 300 robots at Chimelong, and Tekever raises $580 million.
Mondo Robotics' Beni: A Tumbling Robot Camera with Significant Obstacle Avoidance Issues
Mondo Robotics has introduced Beni, a small bipedal robot designed to capture and record video while following users. Equipped with a built-in camera and sensors, Beni can perform tricks like flips and roll while tracking specified objects via an app. However, after several weeks of testing, reviewers found Beni's obstacle avoidance capabilities severely lacking. The importance of Beni lies in its potential as a fun companion for personal activities like sports and pet video recording. Despite its interesting features, the robot struggles significantly with obstacle avoidance, often losing track of its target in cluttered environments. Reviewers noted that even in open spaces, Beni failed to navigate around obstacles effectively, which detracts from its overall usability. Moving forward, it will be crucial to monitor Mondo Robotics' efforts to improve Beni's obstacle avoidance technology. Currently priced at $599, the robot's value is questionable given its primary flaw. If Mondo can address these issues, Beni could become a more appealing option for users engaged in various activities, but until then, recommendations are limited.
Robot Cameras Obstacle Avoidance Consumer Robotics Video Technology
Noin Introduces GLOW Architecture for Instant Learning in Robotics
Noin has unveiled the GLOW architecture, a generative learning framework aimed at enhancing robot learning capabilities. This new technology allows robots to learn tasks instantly without the need for extensive data collection or predefined processes, addressing the challenges posed by dynamic real-world environments. GLOW's core innovation lies in its ability to enable robots to learn tasks through a single demonstration, transforming visual input and feedback into actionable tasks. The significance of GLOW is underscored by its potential to revolutionize how robots acquire skills, moving away from traditional methods that require separate strategies for each task. Instead, GLOW aims to equip robots with generalized capabilities that can be applied across various objects, environments, and tasks. This shift could lead to more efficient and adaptable robotic systems, as users will no longer need to break down their requirements into machine instructions. Future developments to watch include Noin's ongoing commitment to advancing the GLOW technology, which integrates visual understanding, spatial reasoning, task planning, and action generation within a unified model. The architecture has already shown promising results in various evaluations, indicating its effectiveness in physical scene understanding and task execution, paving the way for enhanced robotic intelligence and adaptability.
Robotics Generative Learning AI Embodied Intelligence
The Importance of a Comprehensive Network for Robotics: Insights from the 2026 Information Communication Expo
The 2026 China International Information Communication Expo highlighted the necessity of a comprehensive network for robotics, showcasing advancements in embodied intelligence. The expo featured technologies essential for robotic tasks, including perception, decision-making, motion control, and human-robot interaction, emphasizing the integration of these elements for effective operation. This focus on connectivity is crucial as robots must adapt to various environments, ensuring safety and efficiency in tasks ranging from industrial applications to healthcare. The expo demonstrated how robots can be integrated into smart factories and homes, where they must understand human needs and operate safely in shared spaces. The success of robotic systems relies not only on their ability to perform complex actions but also on their operational stability and cost-effectiveness. Looking ahead, the 2026 initiative for humanoid robots and embodied intelligence aims to enhance real-world training and application validation. The emphasis on reliable real-time control, stable connectivity, and effective data utilization will be key to advancing robotic capabilities and ensuring their successful deployment in diverse environments. No further timeline was disclosed at the time of publication.
Embodied Intelligence Robotics Technology Smart Factories Healthcare Robotics
Academician Zheng Nanning Discusses Embodied Intelligence Beyond Humanoid Robots
Academician Zheng Nanning, Director of the Artificial Intelligence and Robotics Institute at Xi'an Jiaotong University, published an article discussing the growth of embodied intelligence in the physical world. He emphasized that a key direction for artificial intelligence is enabling machines to not only process information but also to flexibly perform various tasks in dynamic environments. This includes precise operations on industrial production lines, autonomous navigation in complex terrains, and dynamic movements in exhibition settings. Zheng clarified that embodied intelligence is not synonymous with humanoid robots; rather, it focuses on whether intelligent systems can truly engage with real-world environments, forming a closed loop of perception, cognition, decision-making, action, and feedback. The evaluation of embodied intelligence should be based on its ability to solve real-world problems, not merely its physical form. He likened understanding embodied intelligence to an infant's process of learning about the world through interaction with their environment. To enable machines to act in the world, embodied intelligence requires various capabilities, including the formation of world models, multimodal representation, causal reasoning, and the integration of generation and action. The development of embodied intelligence relies on high-quality multimodal data and realistic simulation environments. Current limitations include the black box problem, computational constraints, and an immature industrial ecosystem. In sectors like industrial manufacturing, healthcare, and urban governance, intelligent systems are evolving from mere tools to proactive systems capable of continuous learning and adaptation.
Embodied Intelligence Artificial Intelligence Robotics Machine Learning
University of Washington Unveils 1-Gram Jumping Robot with Accurate Height Control
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Jumping Robots Micro Robotics Robotics Engineering Automation Technology
China’s Dexterous Hand Industry, 2026 Part 1 — The Demand Picture
MIR Databank puts China dexterous hand shipments above 30,000 units in 2026, yet only 3.5% of 2025 demand came from factory production. Part 1 of 3.
China’s Dexterous Hand Industry, 2026 Part 2 — The Technology
Linkage, tendon, direct drive or hybrid — and why hardware is converging while the DL1–DL5 manipulation scale decides who wins. Part 2 of 3.
ROBOTTODAY WEEKLY BRIEFING August 24 – 28, 2026
This week in robotics: XPeng's Dogotix raises over $900 million at a $6.3 billion valuation, Unitree's post-IPO slump revives bubble talk, Beijing's World Humanoid Robot Games close with a shift to real-world tasks, and Teradyne sues JAKA at Europe's Unified Patent Court. August 24–28, 2026.
WRC 2026 Core Components: 137 Robot Parts Suppliers
The third report in the WRC 2026 series. Report 1 mapped all 248 exhibitors by category and Report 2 covered the 139 that build finished robots. This one covers the 137 that build what goes inside them.
WRC 2026 Exhibitor Map: 248 Robotics Companies Mapped
We classified 248 WRC 2026 exhibitors with the RobotToday taxonomy. Motion & Actuation leads at 75 companies, Humanoid at 72, and compute stays thin at 12. (Modified 2026-08-27)
Handbook of Marine Craft Hydrodynamics and Motion Control Thor I. Fossen — RSF Specialist Shelf · Book S1 of 6
Added mass, wave loading, DVL navigation: the marine GNC reference for USV, ROV and AUV service engineers. The hardest book on the RSF list, 5/5.
Small Unmanned Aircraft: Theory and Practice Beard & McLain — RSF Specialist Shelf · Book S2 of 6
Beard & McLain derive the fixed-wing autopilot the RSF Top 10 never does: successive loop closure on roll, pitch, altitude and airspeed. 4/5.
Reinforcement Learning: An Introduction Richard S. Sutton & Andrew G. Barto — RSF Specialist Shelf · Book S5 of 6
“It's the AI” will not satisfy an OEM support call. Sutton & Barto turn that into something a service engineer can investigate. Free PDF, Part I 4/5.
Integrated Joint Actuators 2026: Technology, Global Suppliers & China's Leap Forward
From rotary joints hitting 530 N·m+ to linear joints with planetary roller screws, integrated actuators are replacing the three-part drivetrain. Full supplier map: Tihu, PhyArc, RobStride, Schaeffler, maxon — and China's path from 1,000-RMB modules to global standard-setter.
