GPC: Large-Scale Generative Pretraining for Transferable Motor Control

• URL: http://arxiv.org/abs/2606.29148v1
• Date: Sun, 28 Jun 2026 02:05:11 GMT
• Title: GPC: Large-Scale Generative Pretraining for Transferable Motor Control
• Abstract summary: We introduce Generative Pretrained Controllers, which leverage tokenization and next-token modeling to create reusable generative controllers.<n>Our framework utilizes end-to-end reinforcement learning to jointly optimize a "motion vocabulary"<n>The generative controller exhibits a variety of natural emergent behaviors, such as responsive behaviors to perturbations and recovery behaviors after falling.
• Score: 13.694660962373703
• License: http://creativecommons.org/licenses/by/4.0/
• 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), along with a corresponding control policy that can map the discrete codes to physics-based controls. After the "codebook" has been learned, the underlying structure of this large vocabulary is modeled by training a GPT-style autoregressive transformer, leading to a powerful generative controller that generates controls for a physically simulated character by performing next-token prediction. Once the generative controller has been trained, we propose a suite of adaptation techniques for finetuning the controller for new downstream tasks. Our proposed framework greatly simplifies the training process compared to previous tokenized methods, and achieves a 99.98% success rate in reproducing a vast corpus of motion clips. The generative controller exhibits a variety of natural emergent behaviors, such as responsive behaviors to perturbations and recovery behaviors after falling. This results in highly robust general purpose controllers for a variety of downstream applications.

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