Category Robotics Classification

Calico Life Sciences uses Co-Scientist to connect scattered findings and generate new leads in aging research.

Filippo Menolascina uses Co-Scientist to identify new liver disease treatments and explain why existing drugs only help certain patients.

Co-Scientist unites Boston Children’s Hospital and MIT’s labs to explore new RNA-based treatments for ALS.

Stanford geneticist uses Co-Scientist to help find new treatments for chronic liver disease and liver fibrosis.

Learn how our WeatherNext AI model help forecasters give communities unprecedented time to prepare ahead of the historic Hurricane Melissa.

Gemini 3.5 is built to help you execute complex, agentic workflows.

For too long, material movement has been treated as a background function. But advanced motion and robotics are changing that by giving manufacturers the tools to treat material flow as a strategic and competitive lever that they can tune, optimize, and adapt.

Claire chatted to Gavin Kenneally from Ghost Robotics about robot dogs for defence, security, and public safety.

Gavin Kenneally is the Co-Founder and CEO of Ghost Robotics, a company that has gained a reputation for pushing the boundaries of legged robotics technology. In his current role, Gavin spearheads a team of highly skilled engineers and researchers who share his passion for creating advanced robotics systems. Previously, he was Head of Product at Ghost Robotics, responsible for the mechanical design of the company’s flagship product: the Vision 60 Q-UGV. Gavin has a PhD in Mechanical Engineering from the University of Pennsylvania and has authored six academic papers.

NASA is testing a next-generation space computer chip that could give spacecraft the ability to operate far more independently in deep space. The radiation-hardened processor is showing performance levels hundreds of times beyond current spaceflight computers while surviving punishing tests designed to mimic the harsh conditions of space. The technology could enable AI-powered spacecraft, faster scientific discoveries, and smarter missions to the Moon and Mars.

Built for flexible safety architectures, Banner Engineering's I/O blocks support EtherNet/IP and CIP Safety™ with configurable inputs for e-stops, light curtains, and switches. The in-series diagnostics function reduces cable usage by supporting up to 192 safety devices per block with device-level status. The RSio series is innovative, scalable, and robust, making it ideal for large conveyor networks and modular machine designs.

Banner Engineering Q45x sensors provide high-resolution, three-axis vibration data to enable predictive maintenance across industrial equipment. The battery-powered design operates for multiple years without wiring, simplifying installation and scaling across assets. Integrated high-frequency enveloping (HFE) isolates early-stage faults such as bearing wear and lubrication issues, improving diagnostic accuracy in noisy environments. These sensors feature IP67-rated enclosures and wireless connectivity through Sure Cross networks, offering reliable condition monitoring in harsh industrial settings while reducing downtime and maintenance complexity.

XVB7 Tower Lights are sleek, 70mm modular signal lights designed for energy efficiency and high visibility. They support multiple lighting modes (steady, blinking, rotating, and flashing) and offer flexible modular configuration with USB and IO-Link connectivity. The system includes customizable sound options up to 102dB and features push-in terminals and M12 connections, reducing wiring time by up to 95%.

These collaborative robots offer up to 2kg payload, 660mm reach, and four- or six-axis motion for assembly, pick-and-place, machine tending, quality control, and education. The integrated I/O, Ethernet, WLAN, and software support simplify automation in compact workspaces. These cobots employ a human-centric design.

As robots enter hospitals and care facilities, questions remain about whether they actually make care easier for the people who give and receive it. A new Cornell Tech-led study approaches that challenge by inviting health care workers, long-term care residents, and community members to help design the robots themselves.

This article examines how an integrated autonomous picking system — combining a lightweight 6-DOF robotic arm, depth-camera-based vision, adaptive grasping, and intelligent task management — was developed and deployed to address these challenges.