Reward AI Emerges from Stealth with OM-1 for Zero-Shot Human-to-Robot Manipulation
- Published
- Source
- HumanoidsDaily
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.
Original report
Reward AI Exits Stealth with OM-1, Pitching Zero-Shot Human-to-Robot Manipulation
HumanoidsDaily · [email protected] (Humanoids Daily Staff)This briefing is an independently written summary based on publicly available reporting and is provided for industry information and news discovery. The original report and source publication are credited and linked where applicable. RobotToday does not claim ownership of third-party source material.
Rights concerns? Contact [email protected] with the relevant URL and details. We will review the matter and take appropriate action where warranted.
More related news
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
Related Suppliers
Reward AI
United States
Bay Area robot-learning startup founded 2025; its OM-1 policy learns manipulation only from human demos captured with a 7-DOF wearable hand.
sudo robotics 苏度科技
China
Shanghai embodied-AI company founded in 2025 by Han Zheng and Hao Su; its #sudo R1 robot system picks unseen objects using a model trained only in simulation.
Allen Institute for Artificial Intelligence
United States
Seattle non-profit AI institute founded by Paul Allen; releases fully open robot models (MolmoAct, MolmoBot) and simulators (AI2-THOR, MolmoSpaces).
Hubei Humanoid Robot Innovation Center 湖北人形机器人创新中心
China
Wuhan public platform opened in June 2025 to train humanoid robots and collect embodied-AI data in 23 simulated scenes; led by a Hubei state tech investor.
Hebbian Robotics
United States
San Francisco YC S26 start-up founded 2026 that builds HFlow, an open-source SDK for checking and curating robot training data.
ATR Intelligent Robotics and Communication Laboratories
Japan
Kyoto research laboratory within ATR (est. 1986) studying human-robot interaction, cybernetic avatars and social robots such as Robovie and Geminoid.
Center for Intelligent Robotics (CIR) at KIST
Korea, Republic of (South)
South Korean government research center at KIST focused on intelligent robotics and human-robot interaction since 1966.
Xense Robotics (Qianjue Robotics) 千觉机器人
China
Shanghai startup founded in 2024 by an SJTU tactile-sensing researcher, selling optical tactile sensors and the XTac UMI G1 data-collection gripper.
TouchTronix Robotics
United States
Buffalo, New York startup founded 2025, making thin-film tactile sensors, gripper fingertip modules and tactile data-collection gloves for robot learning.
Abaka AI
United States
Palo Alto AI data firm (est. 2019) annotating sensor data for embodied AI, autonomous vehicles, and generative AI; serves 1,000+ industry partners.
Humanola
United States
US start-up founded in 2025 offering VR teleoperation software for humanoids and robot arms and turning each operator session into robot-learning training data.
Intelligence Factory
United States
New York startup founded 2026 (YC P26) training robot manipulation foundation models on human demonstration data recorded with custom sensing gloves.
Robotics needs a service framework.
RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.
Join the RobotToday community on LinkedIn
Daily robotics news, in-depth analysis, conference highlights, and discussions with professionals worldwide.
