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Surgical Robotics, A curated analysis

Enjoy navigating a free and resourceful website on surgical robots.

Interactive, rich, dynamic entry-point to all useful information, this platform lets you review and search complete data sheets about surgical robots: description, specialties of use, characteristics, analytical data, feedback from surgeons.

Every robot listed on this platform is marketed, CE marked and/or FDA approved.

This platform was built by Bryan Kheirallah, after successfully presented his master’s thesis at the EPFL, and in collaboration with the EPFL LASA and the SFITS.

Advancing Mobile Robotics: ARM Laboratory’s robots designed for Inspections and Maintenance operations in Harsh Environments

In the demanding world of industrial maintenance, human operators routinely confront hazardous conditions while performing dangerous tasks in confined, elevated, or degraded environments. The maintenance industry requires systems that can reliably operate in such harsh contexts, executing complex operations like detailed inspections, surface treatments, and repairs while preserving human health.

The Automation, Robotics, and Machines Laboratory (ARM Lab) at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) is revolutionizing how we approach safety and efficiency in high-risk industrial sectors. With a focus on human-centred robotics, the lab designs versatile mobile systems that take care of inspections and maintenance tasks even in extreme conditions — confined spaces, towering heights, and across corrosion, ice, or uneven terrain. The mobile robotic division of ARM Lab builds advanced mobile robots embedding industrial technologies with cutting-edge advancements to create platforms able to work and collaborate with human operators. This collaborative approach ensures robots enhance rather than replace human skills, fostering intuitive teamwork in dynamic settings. Beyond hardware development, the team innovates standard control laws with sophisticated AI algorithms to promote robotic-based maintenance policies—making inspections and repairs not just automated, but intelligently adaptable and executable with precision. Examples of platforms include UMA (Universal Maintenance Automata), a robust wheeled-based heavy-duty climbing robot introduced in 2020, engineered for stable performance during intensive repair tasks. The platform has been honored with the Innovation Radar Price in 2019 and the DINNO Award in 2021 for its forward-thinking design. Evolving from this foundation, the first reconfigurable and climbing quadruped GRACE has been developed by ARM. GRACE offers agile mobility across varied landscapes and overcoming uneven surfaces with obstacles, supporting a full range of maintenance activities.

ARM has already proven the benefits of its robot in real-world applications. In a groundbreaking milestone, an ARM Lab robot completed the operational bridge inspection in August 2025, navigating a highway structure in Switzerland through narrow passages (as tight as 50 x 70 cm), pitch darkness, and obstacles like cables and pipes. The system scanned about 150 meters of embedded steel reinforcements with exceptional accuracy while minimizing human exposure to hazards. Building on this, the lab showcased its solutions at the ROBOTX Innovation Day at the ETH Zurich in 2025.

Through strategic industry partnerships, the ARM lab is reshaping maintenance in harsh environments, driving safer, smarter, and more sustainable operations on a global scale.

Robo-Gym. Smart Interface to Accelerate the Human-Robot Teamwork

In many industrial domains, full automation remains either technically infeasible, economically impractical, or too rigid to accommodate the diversity of real-world tasks and objects. In such contexts, cobots provide an attractive alternative, enabling flexible human–robot collaboration in which human adaptability and dexterity complement robotic precision and endurance.

The Robo-Gym at SUPSI, developed and coordinated by the ARM Lab (Automation, Robotics and Machines Laboratory), was established within the EU project Fluently as a hub for interactive human-robot training. As the facility’s coordinator and principal developer, the ARM Lab introduced a speech-based, multimodal interaction platform that breaks free from rigid programming while remaining robust in noisy factory environments. The platform unites three devices in one smart interface: RealWear Navigator 500 (100 dB noise-cancelled voice + assisted-reality guidance); H-Fluently (mobile with on-device ASR – Automatic Speech Recognition and MSE – Mental State Evaluation, privacy-preserving); R-Fluently (PC hosting containerized AI modules: NLU- Natural Language Understanding, HTN – Hierarchical Task Network planning  and Artificial Vision). Communication across the system relies on WebRTC for low-latency audio streaming, MQTT for device messaging, and ROS2 for inter-module communication.

New tasks can be taught through a hybrid approach that combines speech with hand guidance, significantly reducing the learning curve and improving operational efficiency. On R-Fluently, a voice-driven module adjust the robot’s autonomy level, reallocate tasks when needed, and reconfigure the system rapidly for new applications. This makes the system particularly well suited to contexts where partial automation is preferable to full automation. For instance, experimental evaluations conducted in a disassembly workflow revealed a significant drop in programming time for operations like unscrewing, from about 500 s with Teach Pendant programming to 75 s using speech plus hand guidance (85% reduction). While ASR accuracy declined with diverse user accents and noisy environments, falling from about 75 percent under normal conditions to 64 percent at 80 dB noise levels, NLU exhibited remarkable robustness, maintaining accuracies of 86 percent and 81 percent in normal and noisy settings respectively.

This ARM Lab  implementation demonstrates that by combining noise-resilient hardware, privacy-preserving speech recognition and high-level semantic understanding, the developed smart interface substantially lowers the barrier for non-expert operators in real-world scenarios while preserving the flexibility needed for effective human–robot teamwork.

Four SNSF Advanced Grants go to ETH Zurich researchers

Four ETH Zurich professors have been awarded SNSF Advanced Grants, receiving a total of 8.5 million CHF over five years. These grants, ranging from 1.9 to 2.7 million CHF per recipient, support top researchers and were introduced as a temporary measure after Switzerland lost access to ERC funding. This is the final round of SNSF Advanced Grants, as Swiss researchers will be eligible to apply for ERC Advanced Grants again in the next round.

https://ethz.ch/en/news-and-events/eth-news/news/2025/01/four-snsf-advanced-grants-go-to-eth-zurich-researchers.html