一种带切换隐反馈控制器的技能获取概率模型
A Probabilistic Model for Skill Acquisition with Switching Latent Feedback Controllers
AI总结：
本研究将混合密度网络重新解释为隐状态条件下的反馈控制器库，构建概率图模型将技能获取建模为隐空间分割，既提升任务成功率与抗观测噪声鲁棒性，也改善了实体机器人的模型部署效果。
摘要：
操作任务通常由多个子任务组成，每个子任务对应一项不同的技能。掌握这些技能对机器人至关重要，因为它能提升机器人的自主性、效率、适应性以及在所处环境中工作的能力。从演示中学习可让机器人无需从零开始就能快速习得新技能，而演示通常会将各项技能按顺序排列以完成任务。基于行为克隆的从演示中学习方法通常依赖混合密度网络输出头来预测机器人动作。在本研究中，我们首先将混合密度网络重新解释为以隐状态为条件的反馈控制器（或技能）库。这一解读源于一项观察：单层线性网络在功能上等同于经典反馈控制器，其网络权重对应控制器增益。我们利用这一见解推导了一个结合上述要素的概率图模型，将技能获取过程描述为隐空间中的分割问题，其中每个技能策略在该隐空间中充当反馈控制律。我们的方法不仅显著提升了任务成功率，还增强了在使用人类演示训练时对观测噪声的鲁棒性。我们的实体机器人实验进一步表明，这种诱导产生的鲁棒性提升了模型在机器人上的部署效果。
英文摘要：
Manipulation tasks often consist of subtasks, each representing a distinct skill. Mastering these skills is essential for robots, as it enhances their autonomy, efficiency, adaptability, and ability to work in their environment. Learning from demonstrations allows robots to rapidly acquire new skills without starting from scratch, with demonstrations typically sequencing skills to achieve tasks. Behaviour cloning approaches to learning from demonstration commonly rely on mixture density network output heads to predict robot actions. In this work, we first reinterpret the mixture density network as a library of feedback controllers (or skills) conditioned on latent states. This arises from the observation that a one-layer linear network is functionally equivalent to a classical feedback controller, with network weights corresponding to controller gains. We use this insight to derive a probabilistic graphical model that combines these elements, describing the skill acquisition process as segmentation in a latent space, where each skill policy functions as a feedback control law in this latent space. Our approach significantly improves not only task success rate, but also robustness to observation noise when trained with human demonstrations. Our physical robot experiments further show that the induced robustness improves model deployment on robots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
