GPC: Large-Scale Generative Pretraining for Transferable Motor Control

2026-06-27

AI Summary

This work proposes a general-purpose physics-based character controller capable of executing tasks with natural, realistic, and diverse motions. The approach discretizes motion data using Finite Scalar Quantization (FSQ) and integrates a GPT-style autoregressive Transformer with end-to-end reinforcement learning to build a transferable generative motion controller amenable to downstream task fine-tuning. A key innovation lies in the joint optimization of the discrete action vocabulary and the control policy, replacing conventional pipeline-based training procedures. Experimental results demonstrate that the method achieves a 99.98% motion reproduction success rate on large-scale motion datasets and exhibits robust behaviors such as perturbation response and fall recovery, proving effective across a range of downstream control tasks.

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.

Problem

• motor control
• character animation
• generative modeling
• reinforcement learning
• motion synthesis

Innovation

• Generative Pretrained Controllers
• Finite Scalar Quantization
• Autoregressive Transformer
• Tokenized Motion Control
• Reinforcement Learning