NVIDIA 提出 GPC：预训练运动控制器，可像 GPT 一样微调新任务
NVIDIA 把 GPT 的套路用在运动控制上，预训练一个模型就能微调干不同活，物理模拟里跑得又自然又实时。
NVIDIA 在 SIGGRAPH 上发表 Generative Pretrained Controllers (GPC)，将运动技能编码为离散 token。GPC 使用 transformer 进行下一个 token 预测，类似 GPT 的预训练范式。它在 600+小时运动数据上训练，可实时运行在物理模拟中。同一个预训练控制器通过微调即可适配新任务，生成自然且物理合理的交互动作。
Most motion papers tailor one controller to one specific task. This year at SIGGRAPH, our research team asks: can motor control itself be pretrained and reused? Generative Pretrained Controllers, or GPC, turn motor skills into a vocabulary of discrete tokens and train a transformer-based generative controller through next-token prediction. Just like GPT, the same pretrained controller can then be fine-tuned to solve new tasks. Trained on 600+ hours of motion, GPC runs in real-time inside a physics simulation, producing natural and physically grounded behaviors for interactive control. Your browser does not support the video tag. 🔗 View on Twitter 💬 5 🔄 8 ❤️ 54 👀 4108 📊 13 ⚡
