让机器人在未知环境中安全预判并避让其他机器人的行为
Safe Learning Predictive Control for Ego-World Robotic Systems
- 用在线学习推断周围机器人的潜在行为策略
- 基于稀疏变分高斯过程实时更新策略，保证安全控制
- 适合需要与未知智能体共存的自主导航场景
在共享环境中的安全自主导航，需要能够预判并响应周围机器人潜在的行为。本文提出SOWL-MPC，一种面向新型‘自我世界’机器人框架的安全学习预测控制策略。在此设定下，世界机器人的控制策略未知，自我机器人通过数据学习该策略并执行安全动作。该架构结合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。仅依赖噪声状态观测，方法通过在线变分条件（OVC）在流式数据上更新潜在世界策略的后验分布。学习到的策略通过近似矩传播方案传递至非线性世界动力学，并输入不确定性感知的模型预测控制（MPC），从而实现自我机器人的安全机动。在ROS 2中通过大量蒙特卡洛虚拟实验验证了SOWL-MPC的实时可行性与安全保证，并在室内场地的真实机器人硬件上完成验证。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。