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Using large-scale GPU parallelized reinforcement learning in Isaac Sim, we are able to obtain a robust whole-body controller for Taku optimized for precise loco-manipulation.

Dyna Robotics

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We open-sourced all components of the HTD whole-body controller (WBC): -Play our WBC on your browser: -Main HTD codebase entry: (data collection and HTD policy soon!) Highlighted features: -Lightweight decoupled WBC that can be trained on a single GPU -A stable control base for humanoid loco-manipulation (in a mobile-manipulation style) -Support a wide range of body poses including extreme ones -Isaac Sim/Lab training, zero-shot deployment

We open-sourced all components of the HTD whole-body controller (WBC): -Play our WBC on your browser: -Main HTD codebase entry: (data collection and HTD policy soon!) Highlighted features: -Lightweight decoupled WBC that can be trained on a single GPU -A stable control base for humanoid loco-manipulation (in a mobile-manipulation style) -Support a wide range of body poses including extreme ones -Isaac Sim/Lab training, zero-shot deployment

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Mobile-TeleVision follows our previous idea on upper-body and lower-body separation for control: 1⃣We train RL for lower-body locomotion and standing; 2⃣We use IK and retargeting directly for upper-body for high-precision and smooth manipulation. We avoid using RL for upper-body motion tracking. This is because fundamentally it is hard to avoid errors with RL tracking, and sim2real gap brings another gap. At this point, we still find using IK is more practical and precise.

Mobile-TeleVision follows our previous idea on upper-body and lower-body separation for control: 1⃣We train RL for lower-body locomotion and standing; 2⃣We use IK and retargeting directly for upper-body for high-precision and smooth manipulation. We avoid using RL for upper-body motion tracking. This is because fundamentally it is hard to avoid errors with RL tracking, and sim2real gap brings another gap. At this point, we still find using IK is more practical and precise.

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notes on my reinforcement learning for rocket league side quest so far: i’m in awe that — we live in a time where i can train a reinforcement learning model that is learning to play rocket league and at the same time, test community made rocket league models. where, both processes can run in parallel & locally on my hardware. in the game window — i’m playing against a reinforcement learning ppo model that’s running locally. and in the vscode window — i’m training my own reinforcement learning model (just a dummy example script for now). also, the rocket league botting ecosystem has incredible tooling where you can test out different reinforcement learning ideas against other algorithms + it has great documentation for linux and windows. it’s honestly a beautiful hidden gem of a rabbit hole :)

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Putting our whole body controller through more tests. It's such a strong foundation for skill development, the next few months are going to be a lot of fun.

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