Abstract
Ensuring safe and effective collaboration between humans and autonomous legged robots is a fundamental challenge in shared autonomy, particularly for teleoperated systems navigating cluttered environments. Conventional shared-control approaches often rely on fixed blending strategies that fail to capture the dynamics of legged locomotion and may compromise safety. This paper presents a teleoperator-aware, safety-critical, adaptive nonlinear model predictive control (ANMPC) framework for shared autonomy of quadrupedal robots in obstacle-avoidance tasks. The framework employs a fixed arbitration weight between human and robot actions but enhances this scheme by modeling the human input with a noisily rational Boltzmann model, whose parameters are adapted online using a projected gradient descent (PGD) law from observed joystick commands. Safety is enforced through control barrier function (CBF) constraints integrated into a computationally efficient NMPC, ensuring forward invariance of safe sets despite uncertainty in human behavior. The control architecture is hierarchical: a high-level CBF-based ANMPC (10 Hz) generates blended human-robot velocity references, a mid-level dynamics-aware NMPC (60 Hz) enforces reduced-order single rigid body (SRB) dynamics to track these references, and a low-level nonlinear whole-body controller (500 Hz) imposes the full-order dynamics via quadratic programming to track the mid-level trajectories. Extensive numerical and hardware experiments, together with a user study, on a Unitree Go2 quadrupedal robot validate the framework, demonstrating real-time obstacle avoidance, online learning of human intent parameters, and safe teleoperator collaboration.
Abstract (translated)
确保人类与自主腿足机器人之间安全有效的协作是共享自主系统中的一个基本挑战，特别是在处理复杂环境的远程操作系统中。传统共控制方法通常依赖于固定的融合策略，这些策略无法捕捉腿足运动的动力学特性，并可能影响安全性。本文提出了一种基于感知操作员、关注安全性的自适应非线性模型预测控制（ANMPC）框架，用于四足机器人在避障任务中的共享自主驾驶。 该框架采用固定的人机行动仲裁权重，但通过使用嘈杂的理性Boltzmann模型来模拟人的输入，并利用从观测到的操作杆命令中在线调整参数的方法来增强这一方案。安全机制则通过将控制屏障函数（CBF）约束整合到计算效率高的NMPC中实现，在存在人类行为不确定性的情况下仍能确保安全集的前向不变性。控制系统架构是分层设计的：高级别的CBF基础ANMPC（10Hz）生成融合的人机速度参考；中级别的动力学感知NMPC（60Hz）采用简化的单刚体（SRB）动力学模型来跟踪这些参考值；低级别的非线性全身控制器（500Hz）通过二次规划实施完整的动态模型以追踪中间层级的轨迹。 对Unitree Go2四足机器人进行了广泛的数值和硬件实验，以及用户研究，验证了这一框架的有效性。结果表明该系统实现了实时避障、在线学习人类意图参数，并且安全的人机协作。
URL
https://arxiv.org/abs/2509.22815
