发表机构
University of Texas at Arlington; Carnegie Mellon University（德克萨斯大学阿灵顿分校; 卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对人机协作中安全与可解释性的双重挑战，提出SAGE框架，通过SAIL内化安全约束和TALO协调多智能体更新，在仿真和物理实验中显著提升成功率并降低碰撞。
AI 中文摘要
多方人机协作面临双重挑战：机器人决策应保持可解释性和可审计性，同时执行的动作必须在物理交互过程中满足安全约束。将可解释的决策树策略与控制障碍函数（CBF）滤波相结合，提供了一种有前景的架构，但在多智能体强化学习中产生了两种学习失配。安全投影改变了应用于环境的动作，而耦合的提议图可能使独立优化的智能体更新与团队级更新错位。我们提出了安全对齐梯度强制执行（SAGE）来解决这两种失配。其屏蔽退火内化层（SAIL）使用可微的有限惩罚提议映射，同时保留精确的CBF二次规划用于执行，保持约束法向敏感性以内化反复激活的安全约束。团队平均李雅普诺夫策略优化（TALO）构建团队感知的更新参考，并应用李雅普诺夫半空间校正来调节独立的智能体更新。使用两个仿人机器人和一个人类伙伴的物理实验证明了部署可行性。在九个仿真场景中，SAGE实现了71.0%的成功率，每千个环境步骤中碰撞步骤为0.5。消融研究表明，直接CBF滤波将碰撞频率降低了98.5%，但成功率从67.3%降至59.3%。SAIL将提议违规减少了48.8%，提议执行校正减少了85.2%，而TALO将更新一致性差距减少了50.8%。
英文摘要
Multi-party human-robot collaboration poses a dual challenge: robot decisions should remain interpretable and auditable, while executed actions must satisfy safety constraints during physical interaction. Combining explainable decision-tree policies with control-barrier-function (CBF) filtering provides a promising architecture but creates two learning mismatches in multi-agent reinforcement learning. Safety projection changes the action applied to the environment, while the coupled proposal graph can misalign independently optimized actor updates with a team-level update. We present safety-aligned gradient enforcement (SAGE) to address both mismatches. Its shield-annealed internalization layer (SAIL) uses a differentiable finite-penalty proposal map while retaining the exact CBF quadratic program for execution, preserving constraint-normal sensitivity to internalize repeatedly active safety constraints. Team-averaged Lyapunov policy optimization (TALO) constructs a team-aware update reference and applies a Lyapunov half-space correction to regulate independent actor updates. Physical experiments with two humanoid robots and a human partner demonstrate deployment feasibility. Across nine simulation scenarios, SAGE achieves a 71.0% success rate with 0.5 collision steps per thousand environment steps. Ablations show that direct CBF filtering reduces collision frequency by 98.5% but decreases success from 67.3% to 59.3%. SAIL reduces proposal violation by 48.8% and proposal-execution correction by 85.2%, while TALO reduces the update-consistency gap by 50.8%.
