Editor-Curated Robotics Industry Briefing - RobotToday

Reward AI Emerges from Stealth with OM-1 for Zero-Shot Human-to-Robot Manipulation

Reward AI has officially launched its foundation model, OM-1, which aims to revolutionize robot manipulation by learning directly from human demonstrations. This approach seeks to eliminate the need for teleoperation and on-robot training, addressing the limitations of traditional methods that can distort training data. The significance of Reward AI's OM-1 lies in its innovative use of the Omnibody stack, which integrates hardware and multimodal data capture to train robots on natural human movements. By utilizing a specialized wearable glove, the company captures nuanced human dexterity without the constraints of typical robotic systems, allowing for more effective training. Looking ahead, Reward AI's approach could reshape the landscape of robotic training and manipulation. The company claims that OM-1 can learn complex tasks with minimal human demonstration data, setting a new standard for efficiency in robot learning. No further timeline was disclosed at the time of publication.

Autonomous Drones Use Virtual Rewards to Outmaneuver Pursuers in Tag Game

Autonomous drones are being trained using virtual rewards to enhance their evasion skills in a high-speed game of tag. This innovative approach focuses on machine learning and decision-making, allowing drones to effectively outmaneuver pursuers and reach their base. The significance of this development lies in its potential applications in various fields, including surveillance, search and rescue, and military operations. By mastering cooperative strategies and rapid responses, these drones could revolutionize how autonomous systems interact in dynamic environments. Looking ahead, it will be important to monitor advancements in drone technology and machine learning algorithms that facilitate these capabilities. No further timeline was disclosed at the time of publication.

TechXplore:Robotics Aug 18, 2026 Robotics