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The power of generative models — now embodied in humanoids.
Announcing DreamControl –– After a year-long research effort at General Robotics — we present a scalable framework for whole-body humanoid control that fuses diffusion priors with reinforcement learning to unlock real-world scene interaction.
Diffusion + RL → natural whole-body skills on real robots.
DreamControl enables humanoids to move beyond locomotion demos → performing natural, human-like skills such as ––
Picking & lifting objects, Opening drawers & doors, Precise punching, kicking, and jumping, Bimanual manipulation tasks
Our key innovation: a diffusion prior over human motion that guides RL, eliminating the need for massive teleoperation datasets, and producing motions that look human while transferring to real hardware.
Trained purely in simulation, deployed on the @UnitreeRobotics G1 humanoid, DreamControl policies run in real time, bridging sim-to-real with unprecedented naturalness.
We leverage a novel hybrid edge + cloud infrastructure that runs RL-trained policies on the edge backed by powerful AI models running in the cloud
This is the next step in General Robotics’ journey toward general-purpose humanoid assistants that interact, adapt, and assist autonomously.
Paper: https://t.co/sTnYZHKPNd
Blog: https://t.co/Sm4OFRqBKb
1/n
Engineering teams in robotics face a distinct challenge: they must simultaneously manage data collection, cloud architecture, simulation environments, and Deep ML workflows. The complexity compounds at each layer.
Everyone's obsessed with robot arms.
But the real revolution is in robot eyes.
Vision systems will have to transform aviation before we ever get flying cars.
This is where the next trillion-dollar company will emerge.
Believe in what you're building.
Even when everyone says you're wrong.
Even when the "experts" disagree.
Even when it seems impossible.
That's how robotics breakthroughs happen.