Over the weekend coded up an implementation of TWIST2 in MjLab in order to update the framework from the EOL IsaacGym and it is fully public on GitHub: https://t.co/kzLLKH1n9T.
Credits to @ZeYanjie and @kevin_zakka for their amazing work!
@PTrubey@chris_j_paxton For me this is more to show that we don’t need full body motion generation to still accomplish tasks - a starting point to build up from, since you can delegate a lot of the higher level planning to existing vlms with “common sense”, i.e. they provide good sparse waypoints
@chris_j_paxton To be fair I think people use it cuz it’s a UMI clone haha (not that it’s bad)
I feel wrist cameras are for vision coverage because current vla-style models rarely have memory
1/ Introducing CLAW 🦀 — a pipeline for scalable generation of language-annotated whole-body motion data for the Unitree G1 humanoid.
Joint work with @ThomasYuxinChen at MSC Lab, UCberkeley
Code: https://t.co/gN45zcSyBX
Tech Report: https://t.co/AtpuSIQjj7
We've just open-sourced the SONIC training code, the training data, the algorithms used to generate the data, and more to come. You now have the full recipe for building SONIC whole-body controllers for your own humanoids. Enjoy!
Code: https://t.co/WAZ1P12shu
Web Demo: https://t.co/sc3yxIKpuJ
mjlab v1.3.0 is out! It's packed with features: a revamped viewer built on mjviser, a preset-based terrain system, simplified actuator config, and new MDP primitives. Here's a Go1 quadruped trained in the stair terrain with the new terrain-aware foot height sensor.
Over the weekend coded up an implementation of TWIST2 in MjLab in order to update the framework from the EOL IsaacGym and it is fully public on GitHub: https://t.co/kzLLKH1n9T.
Credits to @ZeYanjie and @kevin_zakka for their amazing work!
@carlosdponx Also tried infusing safety in the learning controller itself: https://t.co/e70bjRxJEs
I try to bring all of my robots out and let them run in the real world