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.
翻译:在不同情景下安全部署自主机器人需要能够有效适应新环境并同时满足限制条件的代理人。 在这项工作中,我们提出了在动态不确定的情况下维护安全的实用和理论上合理的方法。 我们的方法利用了巴伊西亚元学习的优势来进行最后的适应。 受过训练的离线神经网络功能的清晰度,加上高效的最后一层在线适应,使得能够产生紧凑的自信,这些信任套套套套在模型上对真实动态进行在线调整时,这些套套套在真实动态上相互连接。 我们利用这些信任套来规划保证系统安全的轨迹。 我们的方法处理高度动态不确定的问题,在最初可能无法安全地实现目标的情况下,我们的方法是首先通过“textit{Exploring}”来收集数据和减少不确定性。 在自主的脱机前,我们所获得的信息的清晰度能够被安全地应用。 根据合理的假设,我们的框架可以保证所有制约因素都具有高度概率的满意度,即在整个任务期间。 我们的理论分析还激励了两个在最后的动态不确定状态下实现目标。 我们通过大规模地展示了我们的软体适应能力,通过模拟模型来提高我们的软体能力。