自我世界机器人系统的安全学习预测控制

中文摘要 AI 辅助

在共享环境中的安全自主导航需要具备预测和应对周围机器人潜在行为的能力。本文提出了SOWL-MPC，一种针对新型场景的基于安全学习的预测控制策略，即自我世界机器人框架。在这种情况下，世界机器人的控制策略未知，自我机器人利用数据来学习并执行安全机动。该架构将基于稀疏变分高斯过程（SVGPs）的在线学习机制与滚动时域控制方案相结合。仅依靠噪声状态测量，通过在线变分条件（OVC）在流数据上更新潜在世界策略的后验分布。通过近似矩传播方案将学习到的策略通过非线性世界动力学传播，并输入到不确定性感知模型预测控制（MPC）中，从而实现自我机器人的安全机动。通过在ROS 2中进行广泛的蒙特卡洛虚拟实验证明了SOWL-MPC的实时可行性和安全保证，并在室内场地的真实世界机器人硬件上进行了验证。

英文摘要

Safe autonomous navigation in shared environments requires the ability to anticipate and react to the latent behaviors of surrounding robots. In this paper, we propose SOWL-MPC, a safe learning-based predictive control strategy for a novel scenario, which we name ego-world robotic framework. In this setting, the control policy of the world robot is unknown and the ego exploits data to learn it and perform safe maneuvers. The proposed architecture combines an online learning mechanism based on Sparse Variational Gaussian Processes (SVGPs) with a receding-horizon control scheme. Relying solely on noisy state measurements, our approach infers a posterior distribution over the latent world policy, which is updated on streaming data via Online Variational Conditioning (OVC). The learned policy is propagated through the nonlinear world dynamics using an approximate moment propagation scheme, and fed to an uncertainty-aware Model Predictive Control (MPC), thus enabling safe maneuvering of the ego robot. The real-time feasibility and safety guarantees of SOWL-MPC are demonstrated through extensive Monte Carlo virtual experiments in ROS 2, and validated on real-world robotic hardware in an indoor arena.

发表机构

• University of Bologna（博洛尼亚大学）

机构由 AI 辅助整理，请以论文原文为准。