安全主动动力学学习与控制：一个顺序探索-开发框架
Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework
摘要：
自主机器人在不同场景中的安全部署需要能够有效适应新环境的代理，同时满足约束条件。在这项工作中，我们提出了一个实际的和理论上合理的方法来保持安全的存在动力学的不确定性。我们的方法利用贝叶斯元学习和最后一层适应。离线训练的神经网络特征的表现力，再加上有效的最后一层在线自适应，使得能够推导出紧密的置信集，这些置信集在模型在线自适应时围绕真实动态收缩。我们利用这些信心集规划轨迹，保证系统的安全性。我们的方法处理高动态不确定性的问题，安全地达到目标是可能最初是不可行的，通过第一\textit{exploring}收集数据和减少不确定性，自主\textit{exploiting}获取的信息之前，安全地执行任务。在合理的假设下，我们证明了我们的框架保证了所有约束在任何时候联合，即在总任务持续时间的高概率满足。这种理论分析还激发了最后一层元学习模型的两个正则化器，它们通过减少置信集的大小来提高在线适应能力和性能。我们在仿真和硬件上广泛地展示了我们的方法。
英文摘要：
Safe deployment of autonomous robots in diverse scenarios requires agents that are capable of efficiently adapting to new environments while satisfying constraints. In this work, we propose a practical and theoretically-justified approach to maintaining safety in the presence of dynamics uncertainty. Our approach leverages Bayesian meta-learning with last-layer adaptation. The expressiveness of neural-network features trained offline, paired with efficient last-layer online adaptation, enables the derivation of tight confidence sets which contract around the true dynamics as the model adapts online. We exploit these confidence sets to plan trajectories that guarantee the safety of the system. Our approach handles problems with high dynamics uncertainty, where reaching the goal safely is potentially initially infeasible, by first \textit{exploring} to gather data and reduce uncertainty, before autonomously \textit{exploiting} the acquired information to safely perform the task. Under reasonable assumptions, we prove that our framework guarantees the high-probability satisfaction of all constraints at all times jointly, i.e. over the total task duration. This theoretical analysis also motivates two regularizers of last-layer meta-learning models that improve online adaptation capabilities as well as performance by reducing the size of the confidence sets. We extensively demonstrate our approach in simulation and on hardware.
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