๊ฐ€์ž… ํ›„ ์ดˆ๋Œ€ ๋งํฌ๋ฅผ ๊ณต์œ ํ•˜๋ฉด ๋™์˜์ƒ ์žฌ์ƒ ๋ฐ ์ดˆ๋Œ€ ๋ณด์ƒ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

Axis Robotics Philippines
@axisroboticsPh
Welcome to @axisrobotics in the Philippines ๐Ÿ‡ต๐Ÿ‡ญ A place where you complete daily tasks, contribute data on @base, and help build the future of Physical AI.
๊ฐ€์ž… May 2026
11 ํŒ”๋กœ์ž‰ ์ค‘    418 ํŒฌ
This is the kind of infrastructure Physical AI has been missing. Axis V2 moves beyond static data collection by introducing a continuous feedback loop where every policy failure becomes an opportunity to generate better data and improve the next model iteration. Faster learning, greater scalability, and a data engine that compounds over time. Excited to see this paradigm push Physical AI one step closer to real-world deployment. ๐Ÿš€ #AxisRobotics# #AxisRoboticsPH#
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Today, weโ€™re announcing Axis V2 โ€” the compounding data engine for Physical AI. Axis V2 delivers scalable online post-training to the Axis data engine, solving Physical AIโ€™s biggest bottlenecks: scale, diversity, and iteration speed. The core paradigm shift is straightforward. Rather than relying solely on pre-defined data collection workflows, a trained policy becomes an active component of the data engine โ€” its failure cases dictate what data is collected next. Policy rollout โ†’ Failure states โ†’ Targeted tasks โ†’ Human-gated corrections โ†’ Policy retraining โ†’ Enhanced policy โ†’ New rollout โ™ป๏ธ Failures translate into targeted tasks; human corrections power the next iteration of policy training, and the refined policy uncovers the next set of required data. Data and policy now evolve in lockstep within an uninterrupted feedback loop. Full details are outlined below โฌ‡๏ธ
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