用扩散模型生成动作序列，让机器人更好理解并配合人类操作。
DiSCo: Diffusion Sequence Copilots for Shared Autonomy
- 基于扩散模型规划动作序列，融合用户历史操作
- 在模拟驾驶和机械臂任务中显著提升任务成功率
- 适合需要人机协同控制的复杂系统场景
共享自主性通过结合人类用户与人工智能协作者共同控制复杂系统（如机械臂）来提升性能。当任务具有高维控制、难度大或存在干扰时，训练好的协作者可有效纠正用户操作，且保持与用户目标一致。为显著提升共享自主性表现，我们提出扩散序列协作者（DiSCo）：一种利用扩散策略生成与历史用户操作一致的动作序列的方法。DiSCo通过超参数调节，平衡专家动作一致性、用户意图对齐性及感知响应速度，以用户提供的动作作为扩散过程的种子与内插信息。我们在模拟驾驶和机械臂任务中验证了其有效性，结果表明该方法能显著提升任务性能。
原文摘要 · Abstract (English)
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/
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