Axis Robotics

3 cities. 3 communities. 1 mission. Jakarta 🇮🇩 → Manila 🇵🇭 → Ho Chi Minh City 🇻🇳 — the community has always been our backbone. We've been on the road meeting the builders shaping the future of Physical AI, and we're just getting started. Where should we go next? Drop your city below. 👇

After careful deliberation from the Raydium team in the last 24 hours, we will NOT be leaving to build on the Robinhood chain. We will continue to build for the best trading experience on the best chain: Solana.

Axis Weekly This week we focused on making browser-based robot control smoother, preparing longer-horizon articulated-object tasks, and tightening the path from task data to trained and evaluated policies. The main theme was improving the robotics loop end to end: interaction, replay, verification, training, and evaluation all moved toward workflows that are easier to inspect, reproduce, and explain. Key updates: - Teleoperation: End-effector dragging was rebuilt with smoother joint-space interpolation, reducing arrival jitter and raising the effective control frequency while dragging. - Long-horizon tasks: The task set expanded toward multi-step articulated-object demos, including tasks with hinged or movable objects and four to five meaningful action steps. - Policy release loop: The policy workflow became more explicit by connecting task IDs, model paths, evaluation outputs, success-rate summaries, and visual diagnostics. - Multi-embodiment dataset: We validated dataset generation across multiple robot embodiments, moving toward shared task definitions, rendering, and evaluation surfaces. A closer look at this week’s progress 🧵