Dyna Robotics has unveiled what it describes as a “breakthrough” in robotic autonomy with the release of Dynamism v1 (Dyna-1), the company’s first robot foundation model capable of sustained, high-performance operation on complex dexterous tasks using a pair of stationary robotic arms.
Dyna-1 distinguishes itself through exceptional robustness and reliability, even over extended periods of continuous use.
In a demonstration of its capabilities, Dyna-1 was assigned a production-grade task: folding napkins to restaurant standards.
Over a 24-hour period, the robot autonomously folded more than 700 napkins with a success rate exceeding 99.4 percent, all without human intervention, maintaining a throughput of 60 percent of human speed.
This level of autonomous operation marks a significant advancement in the field of embodied AI. Existing robot foundation models – despite access to diverse datasets and large-scale architectures – often struggle with sustained performance, frequently entering unrecoverable states during prolonged operations.
Success rates for these models tend to plateau at around 80 percent on complex dexterity tasks in single episodes.
Dyna Robotics experienced similar challenges internally with its own advanced baseline models. These systems, while initially effective, typically lost stability after an hour or two of operation, requiring human correction.
To overcome these limitations, the company developed a generalizable method for training robust, autonomous robotic foundation models, with a key innovation being the introduction of a scalable reward model (RM).
This reward model provides fine-grained, accurate feedback across a wide range of robotic interactions, forming the basis for core functionalities such as:
- Autonomous exploration – enabling the robot to intelligently navigate its action space and discover effective behaviors
- Intentional error recovery – allowing the system to detect and recover from execution errors
- High-quality dataset generation – facilitating continuous learning through autonomous operation
Through iterative scaling of this RM-in-the-loop training process, Dyna-1 progressed from requiring frequent human assistance to achieving 8-hour autonomy, then 24-hour autonomy with limited throughput, and now consistent high-throughput operation over 24+ hours.
Napkin folding poses a particularly complex challenge due to the need for precision in separating individual napkins and recovering from unintended actions such as pulling multiple napkins at once.
Dyna-1’s runtime learning, powered by the reward model, enabled it to master these challenges and achieve zero-shot generalization – successfully transferring its skills to a customer environment where it had never trained before.
This capability has also shown strong positive transfer to other manipulation tasks, including laundry folding and cup-filling, the latter of which involves sequential, delicate operations such as object placement, handovers, and tool use. These skills have been validated in real-world customer trials.
Dyna Robotics emphasizes that its goal is to develop foundation models for real-world commercial deployment, targeting not only advanced industrial settings but also accessible applications for everyday businesses such as laundromats, restaurants, and grocery stores.
