Axis Robotics (@axisrobotics) / X

Axis Robotics

417 posts

Axis Robotics

@axisrobotics

Scale Physical AI for the real world. Robot intelligence is not built by a few; it's built by all.

axisrobotics.ai

linktr.ee/axisrobotics

Joined November 2025

Pinned
Axis Robotics
@axisrobotics
Mar 24
Axis is officially LIVE on @base. 🔵 Axis is scaling Physical AI for the real world, contributed by everyone. You can control robots in a virtual world, generate training data at scale, and help build the brain behind tomorrow's robots. All from browser. No hardware needed.
00:00
227K
Axis Robotics
@axisrobotics
Jun 11
Excited to build robotics on @base. Real energy at SuperAI with @baseapac. We’re just getting started bringing robots, AI, and onchain coordination together.
Base APAC
@baseapac
Jun 10
AI is exciting. AI onchain is even more exciting. Great to showcase some of the cutting-edge AI robotics innovations being built on @base to @Hassan_NY, Country Director of @CoinbaseSG, at SuperAI. Featuring @axisrobotics and @InvLambda 🤖🟦
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Axis Robotics
@axisrobotics
Jun 10
It's AXIS.
Kr$na
@krishdotdev
Jun 8
It's not FAANG anymore. It's MANGO.
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Axis Robotics
@axisrobotics
Jun 10
This brand new UI is my life savior
8.4K
Axis Robotics
@axisrobotics
Jun 10
Replying to @axisrobotics
Together, these results suggest that AXIS improves robustness across perception, scene geometry, control initialization, and language-conditioned task execution. This is why we describe AXIS as a growable data engine rather than a static dataset: more structured, validated, and
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Axis Robotics
@axisrobotics
Jun 10
Replying to @axisrobotics
The largest gain comes from Layout, the axis where structured scene diversity matters most. LIBERO-Plus evaluates robustness across seven perturbation axes: Camera, Light, Sensor Noise, Background, Layout, Language, and Robot. The largest absolute gain from AXIS-100% appears
2.3K
Axis Robotics
@axisrobotics
Jun 10
Replying to @axisrobotics
AXIS dataset shows a clear scaling trend. We evaluate three AXIS snapshots under the same architecture, training budget, rollout protocol, and success checkers. The only major variable is the pretraining corpus. Overall LIBERO-Plus success increases monotonically as AXIS
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Axis Robotics
@axisrobotics
Jun 10
Replying to @axisrobotics
We start from the released π0.5 checkpoint, optionally pretrain on AXIS or a matched-volume RoboCasa control, then fine-tune on LIBERO and evaluate on LIBERO-Plus. With AXIS-100% (full dataset), π0.5 improves from 66.5 to 79.4 overall on LIBERO-Plus, a +12.9 point gain. The
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Axis Robotics
@axisrobotics
Jun 10
In our conference submission, we evaluate AXIS as a growable data engine for robot manipulation through three questions: 1. Does AXIS pretraining improve π0.5 on downstream LIBERO-Plus robustness tasks, beyond a matched-volume baseline? 2. Does the gain scale with AXIS data
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Axis Robotics
@axisrobotics
Jun 8
Axis Weekly This week was about making the AXIS loop more scalable end to end: automating data-to-model workflows, testing recovery-driven training, expanding TaskGen coverage, and preparing the dataset and model stack for release. Key updates: - Data-to-model
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Axis Robotics
@axisrobotics
Jun 9
GAxis (✱,✱)☀️ New week. New challenges. New access. We’re giving away 10 BitRobot access codes over the next 72 hours. Winners will get access to SN/04 and start earning rewards from both Axis and BitRobot. To join: 1. Follow @axisrobotics & @BitRobotNetwork 2. Like +
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Axis Robotics
@axisrobotics
Jun 4
Announcing our collaboration with @BitRobotNetwork! Axis is launching SN/04 on BitRobot, the open robotics lab on Solana that coordinates distributed contributors to accelerate Physical AI research. SN/04 is a teleop-in-sim mission where contributors complete web-based robotics
12K
Axis Robotics
@axisrobotics
Jun 8
Replying to @axisrobotics
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Axis Robotics
@axisrobotics
Jun 8
Replying to @axisrobotics
On the model side, we finished the first round of fine-tuning, evaluation, and benchmarking, and are now adjusting the data recipe for better performance. The π0.5 evaluation pipeline has been merged into the real-world stack, while web policy inference can now load model
1.2K
Axis Robotics
@axisrobotics
Jun 8
Replying to @axisrobotics
On the TaskGen side, articulated-object generation has been merged into the automatic generation pipeline. TaskGen now supports objects such as cabinets, dishwashers, and drawers, while staying compatible with the existing randomization workflow. For our customer F******, 27
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