Tensor has open sourced OpenTau, a powerful AI training platform unveiled at CES 2026, aiming to remove proprietary barriers and accelerate transparent, reproducible development of Physical AI systems.
At CES 2026, Tensor Auto Inc. announced the official open-source release of OpenTau, a specialised AI training toolchain designed to accelerate the development of Vision-Language-Action (VLA) foundation models, a critical building block for next-generation Physical and Embodied AI systems. The announcement was made on January 8, 2026.
OpenTau is built to support AI systems that can perceive the physical world, reason through language, and act autonomously. By unifying vision, language, and action within a single multimodal foundation model, VLA architectures are increasingly recognised as the core paradigm underpinning applications such as autonomous driving, robotic manipulation, navigation, and embodied intelligence.
Designed for large-scale AI training, OpenTau prioritises reproducibility, accessibility, and scalability, extending advanced training capabilities beyond closed, proprietary research environments. The open-source platform places strong emphasis on scientific transparency, independent validation, and community-driven experimentation.
“At Tensor, we believe meaningful progress in Physical AI requires transparency,” said Jay Xiao, Founder and CEO of Tensor. “OpenTau is our way of giving back to the research and developer community that has helped advance this field. By open-sourcing our training toolchain, we’re supporting broader collaboration–so everyone can build, experiment, and move faster together.”
OpenTau introduces advanced capabilities including co-training across heterogeneous datasets, discrete action modelling for faster Vision-Language Model convergence, knowledge insulation between model components, VLM dropout techniques to reduce overfitting, and a reinforcement learning pipeline purpose-built for VLA models.
By open sourcing OpenTau on GitHub, Tensor is democratising frontier Physical AI training, enabling transparent experimentation, reproducible results, and independent verification—signalling a broader shift toward open collaboration in embodied intelligence.
