itsLabubu
658 posts
robotics has made huge advances in hardware.
the next challenge is intelligence that can adapt to changing conditions without constant retraining.
axis is building infrastructure designed to make physical ai more flexible, scalable, and capable in the real world.
@axisrobotics
English
@elneno13nhtw @axisrobotics Adis is collecting data to improve the robotics system
English
physical ai requires more than intelligent models.
it requires systems that can learn from feedback, adapt to changing environments, and improve through experience.
axis is building the infrastructure layer designed to support that evolution at scale.
@axisrobotics
English
building intelligent robots is not only a hardware challenge.
the harder problem is creating systems that can continuously learn from new environments, new behaviors, and new data.
axis is focused on solving that layer.
@axisrobotics
English
the future of automation depends on systems that can adapt beyond predefined rules.
axis is exploring how continuous interaction and behavioral feedback can help physical ai become more capable in complex, real-world environments.
@axisrobotics
English
@PavelRyan_ @axisrobotics @AxisRoboticsID @alanaanastasya @0xRyzzu Yes, that's right, data is important for robotics.
English
Everyone talks about robot intelligence.
Few talk about the data required to create it.
@axisrobotics is tackling one of the hardest problems in Physical AI: collecting scalable data for long-horizon and cross-embodiment robotic tasks.
English
setelah event besar top refferal leaderboard sudah selesai, akan ada event besar apalagi nih dari @axisrobotics ?
kita tunggu gebrakan di masa depan nanti👀
keep building
gAxis
Indonesia
proud to be the champion of the referral event from @axisrobotics , thanks for the reward team @plpiaoliang @chris_anm01
English
Axis shows that better robot data matters more than just more data. As Axis dataset size grows, performance keeps improving, especially in camera and layout robustness. The results suggest diverse tasks, scenes, and augmentations help robots generalize better, not just memorize.
Axis Robotics@axisrobotics
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 volume, from 25% to 50% to 100% of data volume? 3. Which perturbation axes benefit the most, and do they match the diversity targeted by our augmentation pipeline? Here, “AXIS” refers to our growable manipulation dataset snapshot built around a Franka Research 3 robot: 207 tabletop tasks across 7 scene categories, 50k+ human demonstrations, and 60k+ task/scene variants produced through cleaning and semantic-preserving augmentation. Findings below 🧵
English
janlupp join event mingguan, pasti bakalan seruu inii mahh @AxisRoboticsID
Axis Robotics indonesia@AxisRoboticsID
jangan lupa untuk join event besok guys!
Indonesia
Join komunitas dan dapatkan point di axis hub
Join here :
hub.axisrobotics.ai/login?invite_c…
Gabung komunitas telegram @AxisRoboticsID, berkumpul bersama para penggiat robot Indonesia, yang humble dan ceria
Upgrade role dan masih banyak lagi yang bisa kalian dapatkan...!!!
Join here t.me/+kKl8pkpdD9w1Y…
Indonesia
Yeah akhirnya kesampaian juga dapat role X dan Y di komunitas @axisrobotics
Thanks semua teman2 di komunitas @AxisRoboticsID
loveuall
Indonesia
Finally got my X-axis role from @axisrobotics
Proud to be part of this robotics project🤖
Rimuro@Hengtzyggltmpn
It's hard to believe how fast time flies while working on tasks and collecting trajectories at @axisrobotics and now I've already reached 328 trajectories. Soon it'll be 500. Keep grinding GAXIS🤖
English
jangan lupa yakk besok ramaikan warga @axisrobotics @AxisRoboticsID , dah intinya Pastikan akun GeoGuessr kalian sudah siap buat besok.........
Indonesia
Udah 1Jt trajektori aja nih AXIS. Satu kebanggaan tersendiri bisa ikut partisipasi sama projek ini🥰
@axisrobotics
Axis Robotics@axisrobotics
1M trajectories generated on Axis. A major milestone for our distributed Physical AI data engine.
Indonesia
Update minggu ini terasa kuat karena @axisrobotics tidak hanya menambah hasil, tetapi juga merapikan cara hasil itu terus diproduksi.
Indonesia
Yang terasa penting minggu ini bukan hanya progresnya, tapi cara @axisrobotics mulai membuat seluruh loopnya lebih rapi, lebih cepat, dan lebih siap tumbuh.
Axis Robotics@axisrobotics
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 automation: We used scripts to speed up and standardize several repetitive but critical workflows. - Continuous-growth training: We completed multi-data-scale training and success-rate comparisons across several failure tasks. - Failure task expansion: A new batch of failure tasks has been pushed to test, expanding the evaluation range for ablations across data scale, data quality, and randomization. - TaskGen: Articulated-object generation is now merged into the automatic generation pipeline. - Model and release prep: We finished the first round of fine-tuning, evaluation, and benchmarking, completed the dataset’s conference submission, and are now improving experimental results for release. Details below 🧵
Indonesia
@axisrobotics 🤝 @BitRobotNetwork collaboration!
Selected contributors get special access and a chance to earn exclusive rewards—grab your access code now via the ticket channel before it's gone
English
@axisrobotics Leveling up 🔥
With the introduction of Long-Horizon (LH) and Multi-Embodiment tasks, Axis is moving beyond simple robotic actions toward more complex, real-world scenarios.
This means:
✅ richer training data
✅ more diverse robot interactions
✅ better adaptability across different robot forms
The goal isn't just collecting more data, it's building smarter and more valuable data for the future of Physical AI. 🚀
Axis Robotics@axisrobotics
We recently launched a new set of robotic data collection tasks, with a focus on long-horizon tasks (LH) and cross-embodiment tasks (Multi Embodiment). These include bimanual teleoperation and task adaptation across different robot morphologies. Why this matters: 1. Axis is moving toward more complex, real-world robotic tasks. 2. Long-horizon tasks make complex data collection more scalable in simulation. 3. Staged checkers turn long tasks into clearer training signals. 4. Cross-embodiment tasks help Axis support multiple robot forms and control modes. 5. Axis is improving both the diversity and complexity of data. 6. The goal is not just more data, but more valuable data. Details below. 🧵
English
1M trajectories generated on Axis.
A major milestone for our distributed Physical AI data engine.
English
Dari postingan dibawah kita jadi ngerti kalo @axisrobotics ga cuma nyari lebih banyak data. Tapi jg nyari data yg lebih kompleks, lebih panjang, lebih bervariasi antar robot, dan lebih bernilai buat ngelatih Physical AI
Axis Robotics@axisrobotics
We recently launched a new set of robotic data collection tasks, with a focus on long-horizon tasks (LH) and cross-embodiment tasks (Multi Embodiment). These include bimanual teleoperation and task adaptation across different robot morphologies. Why this matters: 1. Axis is moving toward more complex, real-world robotic tasks. 2. Long-horizon tasks make complex data collection more scalable in simulation. 3. Staged checkers turn long tasks into clearer training signals. 4. Cross-embodiment tasks help Axis support multiple robot forms and control modes. 5. Axis is improving both the diversity and complexity of data. 6. The goal is not just more data, but more valuable data. Details below. 🧵
Indonesia