arXiv:2606.29148v1 Announce Type: cross Abstract: Developing controllers capable of completing a wide range of tasks in a natural and life-like manner is a key challenge in enabling practical applications of physics-based character animation. In this work, we introduce Generative Pretrained Controllers (GPC), which leverage tokenization and next-token modeling to create general-purpose, reusable generative controllers from large-scale motion datasets. Our framework utilizes end-to-end reinforcement learning to jointly optimize a "motion vocabulary", modeled via Finite Scalar Quantization (FSQ)
Advances in large-scale generative models and reinforcement learning are converging to enable new paradigms in motor control for animated characters and robotics.
This development represents a significant step towards more autonomous and versatile physical AI, impacting animation, robotics, and potentially real-world applications.
The ability to create general-purpose, reusable generative controllers from large datasets shifts the paradigm from task-specific programming to more adaptable, learned behaviors.
- · AI researchers
- · Robotics companies
- · Animation studios
- · Simulation platforms
- · Traditional motion capture industries (potentially)
- · Purely handcrafted animation studios
- · Developers of narrow AI controllers
More life-like and adaptable virtual characters and robots become achievable.
Accelerated development of general-purpose robots and more sophisticated AI agents in physical environments.
Ethical and safety considerations for highly autonomous and adaptable physical AI systems become more pressing.
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Read at arXiv cs.LG