itsLabubu
660 posts
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 🧵
jangan lupa untuk join event besok guys!
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🤖
1M trajectories generated on Axis. A major milestone for our distributed Physical AI data engine.
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 🧵
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. 🧵
