Shared autonomy combines human user and AI copilot actions to control complex systems such as robotic arms. When a task is challenging, requires high dimensional control, or is subject to corruption, shared autonomy can significantly increase task performance by using a trained copilot to effectively correct user actions in a manner consistent with the user's goals. To significantly improve the performance of shared autonomy, we introduce Diffusion Sequence Copilots (DiSCo): a method of shared autonomy with diffusion policy that plans action sequences consistent with past user actions. DiSCo seeds and inpaints the diffusion process with user-provided actions with hyperparameters to balance conformity to expert actions, alignment with user intent, and perceived responsiveness. We demonstrate that DiSCo substantially improves task performance in simulated driving and robotic arm tasks. Project website: https://sites.google.com/view/disco-shared-autonomy/

翻译：共享自主性融合人类用户与AI协同驾驶员的操作，用于控制机械臂等复杂系统。当任务具有挑战性、需要高维度控制或易受干扰时，共享自主性可通过训练有素的协同驾驶员根据用户意图有效修正其操作，从而显著提升任务性能。为大幅改进共享自主性表现，我们提出扩散序列协同驾驶员（DiSCo）方法：这是一种基于扩散策略的共享自主性方案，能够规划与用户过往操作一致的序列动作。DiSCo通过用户提供的操作对扩散过程进行种子初始化与修复，并利用超参数平衡对专家操作的遵循度、与用户意图的一致性以及感知响应性。实验表明，DiSCo在模拟驾驶与机械臂操控任务中显著提升了任务性能。项目网站：https://sites.google.com/view/disco-shared-autonomy/