Brebross
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Brebross
@brebross2
Que sera sera --Contributor @AxisRobotics--
Bergabung Mart 2024
392 Mengikuti386 Pengikut
DoorDash is even tracking their drivers' physical movements. This is actually happening.
AXIS captures that exact same human intelligence — but through a browser sim, not a warehouse full of exoskeletons. Every single demo is then multiplied thousands of times in IsaacSim.
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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
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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
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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
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I am in the @thearcitects genesis. seat #1200.
inscribed once on ethereum.
apply with your evm address ↓
ref ARC-12009C9D arcitects.fun
x.com/thearcitects/s…
Arcitects (build arc.)@TheArcitects
whitelist is open. drop wallet below arcitects.fun
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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
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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 🧵
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