Robot path planning and motion control constitute pivotal areas in robotics research, addressing the challenge of enabling autonomous navigation in environments that are both complex and dynamic.

In a study published in Robot Learning journal, researchers propose a new learning-based path planning framework that allows mobile robots to navigate safely and efficiently using a Transformer model.

BOSTON & DETROIT--(BUSINESS WIRE)--Automate 2025 - Realtime Robotics, the leader in automated collision-free motion planning, control, and optimization, today launched Resolver, a new cloud-based ...

This is the core challenge behind Real2Sim--and the focus of XGRIDS' presence at GTC 2026 (March 16--19, San Jose). XGRIDS' spatial intelligence solution now supports NVIDIA Omniverse NuRec for ...

At NVIDIA GTC, RealSense is showcasing a first-of-its-kind demonstration of autonomous humanoid navigation, reinforcing its ...

SAN JOSE, Calif., March 20, 2026 /CNW/ -- For robots to operate reliably in the real world, they must train in environments that accurately represent it. This is the core challenge behind ...

Robots are getting better at sensing their surroundings, but moving safely through unfamiliar spaces remains a bottleneck.

A US computer vision firm presented its role in making humanoid robots safer and ...

Advanced perception and reasoning software enable safe humanoid navigation in real-world environments, says RealSense.

Humans often use one hand to grasp the branch for better accessibility, while the other hand is used to perform primary tasks ...