用扩散模型实时规划，让强化学习控制器实现多任务角色控制。
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control
- 扩散模型作实时规划器，文本和目标位置驱动动作生成。
- 闭环反馈使控制器在复杂任务中保持物理合理性与稳定性。
- 适合需要自然文本指令的虚拟角色多任务控制场景。
运动扩散模型与基于强化学习（RL）的物理仿真控制各有优势：前者能生成多样化的动作并支持文本等直观控制，后者则提供物理上合理的运动并可直接与环境交互。本文提出CLoSD，一种由文本驱动的基于物理的强化学习控制器，通过扩散生成实现多任务控制。核心思想是利用运动扩散模型作为实时通用规划器，增强鲁棒性。CLoSD构建了两个模块间的闭环交互——扩散规划器（DiP）和跟踪控制器。DiP是一个快速响应的自回归扩散模型，受文本提示和目标位置控制；控制器则为简单稳健的动作模仿器，持续接收来自DiP的运动计划，并从环境中获取反馈。该系统可无缝完成一系列任务，包括导航至目标位置、按文本指令用手或脚击打物体、坐下与起身等。项目主页：https://guytevet.github.io/CLoSD-page/
原文摘要 · Abstract (English)
Motion diffusion models and Reinforcement Learning (RL) based control for physics-based simulations have complementary strengths for human motion generation. The former is capable of generating a wide variety of motions, adhering to intuitive control such as text, while the latter offers physically plausible motion and direct interaction with the environment. In this work, we present a method that combines their respective strengths. CLoSD is a text-driven RL physics-based controller, guided by diffusion generation for various tasks. Our key insight is that motion diffusion can serve as an on-the-fly universal planner for a robust RL controller. To this end, CLoSD maintains a closed-loop interaction between two modules -- a Diffusion Planner (DiP), and a tracking controller. DiP is a fast-responding autoregressive diffusion model, controlled by textual prompts and target locations, and the controller is a simple and robust motion imitator that continuously receives motion plans from DiP and provides feedback from the environment. CLoSD is capable of seamlessly performing a sequence of different tasks, including navigation to a goal location, striking an object with a hand or foot as specified in a text prompt, sitting down, and getting up. https://guytevet.github.io/CLoSD-page/
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