arXiv:2606.03985v1 Announce Type: cross Abstract: We introduce Humanoid-GPT, a GPT-style Transformer with causal attention trained on a billion-scale motion corpus for whole-body control. Unlike prior shallow MLP trackers constrained by scarce data and an agility-generalization trade-off, Humanoid-GPT is pre-trained on a 2B-frame retargeted corpus that unifies all major mocap datasets with large-scale in-house recordings. Scaling both data and model capacity yields a single generative Transformer that tracks highly dynamic behaviors while achieving unprecedented zero-shot generalization to uns
Large-scale motion data and Transformer architectures are now mature enough to be combined for highly effective whole-body control, following breakthroughs in large language models.
This represents a significant leap in humanoid robotics control, making robots more agile, adaptable, and capable of real-world generalization, crucial for their commercial viability.
Humanoid robots can now perform complex dynamic tasks without explicit programming for each motion, accelerating their integration into logistics, manufacturing, and service industries.
- · Humanoid robotics manufacturers
- · Logistics and manufacturing sectors
- · AI model developers
- · Companies relying on traditional robotic control methods
- · Labor-intensive manual tasks
Humanoid robots will become increasingly capable of performing a wider array of tasks in unstructured environments.
This will drive down the cost of labor in many sectors, while increasing productivity and potentially exacerbating societal debates around automation's economic impact.
The enhanced capabilities could accelerate the development of autonomous AI agents embodied in physical forms, creating new forms of human-machine interaction and industrial organization.
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Read at arXiv cs.AI