用外部动态模型实时引导扩散策略，实现灵活可控的机器人行为调整。
DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance
- 通过外部动态模型在去噪过程中提供引导信号，分离控制与策略。
- 在复杂任务中平均引导成功率70%，低质量目标下表现优于传统方法5.4倍。
- 可直接适配预训练模型，适合需要快速响应或偏好定制的部署场景。
在真实世界部署大型复杂策略需要能够根据情境灵活调整行为。现有主流方法如目标条件化需预先训练以覆盖测试时的目标分布。为此，我们提出DynaGuide，一种基于外部动态模型在扩散去噪过程中提供引导信号的策略调控方法。该方法将动态模型与基础策略解耦，具备多目标引导、增强基础策略中低频行为、对低质量目标保持鲁棒性等优势。独立的引导信号使其可直接用于现成的预训练扩散策略。我们在一系列模拟与真实实验中验证了DynaGuide的性能，结果显示在一组具关节结构的CALVIN任务上平均引导成功率达70%，且在低质量目标下比目标条件化提升5.4倍。我们还成功引导真实机器人策略表现出对特定物体的偏好，甚至生成全新行为。视频与更多内容见项目主页：https://dynaguide.github.io
原文摘要 · Abstract (English)
Deploying large, complex policies in the real world requires the ability to steer them to fit the needs of a situation. Most common steering approaches, like goal-conditioning, require training the robot policy with a distribution of test-time objectives in mind. To overcome this limitation, we present DynaGuide, a steering method for diffusion policies using guidance from an external dynamics model during the diffusion denoising process. DynaGuide separates the dynamics model from the base policy, which gives it multiple advantages, including the ability to steer towards multiple objectives, enhance underrepresented base policy behaviors, and maintain robustness on low-quality objectives. The separate guidance signal also allows DynaGuide to work with off-the-shelf pretrained diffusion policies. We demonstrate the performance and features of DynaGuide against other steering approaches in a series of simulated and real experiments, showing an average steering success of 70% on a set of articulated CALVIN tasks and outperforming goal-conditioning by 5.4x when steered with low-quality objectives. We also successfully steer an off-the-shelf real robot policy to express preference for particular objects and even create novel behavior. Videos and more can be found on the project website: https://dynaguide.github.io
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