欺骗博弈：闭合交互式机器人自主系统中的安全-学习循环
Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy
AI总结：
针对机器人安全交互方法忽视运行时学习、导致行为保守的问题，本文提出联合建模物理动力学与学习算法的闭环安全控制范式，并用对抗性强化学习支持贝叶斯信念传播和预训练神经轨迹预测器。
摘要：
对于自动驾驶车辆等机器人系统的广泛部署而言，一项突出挑战是在不牺牲性能的情况下确保与人类的安全交互。现有安全方法往往忽视机器人在运行时学习和适应的能力，从而导致过于保守的行为。本文提出一种用于综合安全控制策略的新型闭环范式，通过联合考虑物理动力学与机器人的学习算法，显式处理机器人不断演化的不确定性及其在未来情景出现时快速响应的能力。我们利用对抗性强化学习，在高维学习动力学下实现可处理的安全分析，并证明本框架能够同时适用于 Bayesian 信念传播，以及通过大型预训练神经轨迹预测器进行的隐式学习。
英文摘要：
An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance. Existing safety methods often neglect the robot's ability to learn and adapt at runtime, leading to overly conservative behavior. This paper proposes a new closed-loop paradigm for synthesizing safe control policies that explicitly account for the robot's evolving uncertainty and its ability to quickly respond to future scenarios as they arise, by jointly considering the physical dynamics and the robot's learning algorithm. We leverage adversarial reinforcement learning for tractable safety analysis under high-dimensional learning dynamics and demonstrate our framework's ability to work with both Bayesian belief propagation and implicit learning through large pre-trained neural trajectory predictors.
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。
