GUARD: Sensitivity-aware Uncertainty Attribution for Safe Traditional and Learning-based Robot Control
GUARD: Sensitivity-aware Uncertainty Attribution for Safe Traditional and Learning-based Robot Control
This paper presents GUARD, a framework that combines probabilistic state estimation with uncertainty identification to establish critical thresholds for robot control. By integrating probabilistic kernel optimization with sensitivity-aware uncertainty attribution, the system forwards estimated uncertainties to controllers to adapt safety margins and initiate safe backup trajectories. Experiments on traditional model predictive control (MPC) and reinforcement learning (RL) controllers show that GUARD reduces collision rates by 44-67% in RL evaluations and decreases backup policy activation by 50-75% under medium-to-high uncertainty profiles, while successfully avoiding collisions in MPC simulations.
The paper introduces GUARD, a framework integrating probabilistic kernel optimization with sensitivity-aware uncertainty attribution to enhance safety margins and trigger backup trajectories for both traditional model predictive control and reinforcement learning controllers, reporting collision rate reductions of 44-67% in RL evaluations based on arXiv experiments.
Source: arXiv Robotics — research abstracts · Read original article ↗
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a reinforcement learning (RL) based controller. The results demonstrate that GUARD reduces collision rates by 44-67% in the RL-based evaluation, while decreasing backup policy activation by 50-75% in the medium- and high-uncertainty profiles. In the MPC simulation, GUARD successfully avoids collisions. By unifying the probabilistic kernel optimization algorithm from robot state estimation with the uncertainty estima
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Source：arXiv Robotics — research abstracts · arxiv.org