[
    {
        "id": "osp-26433",
        "type": "article-journal",
        "title": "SAGE: Safety-Aligned Gradient Enforcement for Human--Robot Collaboration",
        "author": [
            {
                "family": "Li",
                "given": "Yisen"
            },
            {
                "family": "Zhang",
                "given": "Hao"
            },
            {
                "family": "Geng",
                "given": "Ruize"
            },
            {
                "family": "Tseng",
                "given": "Yves"
            },
            {
                "family": "Zhao",
                "given": "Ding"
            },
            {
                "family": "Tseng",
                "given": "H. Eric"
            }
        ],
        "URL": "https://omanscience.com/en/articles/sage-safety-aligned-gradient-enforcement-for-human-robot-collaboration",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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%."
    }
]