In various realistic scenarios, robots must grasp objects without knowing their exact pose, relying on probabilistic estimations. The Value of Assistance (VOA) measure evaluates the impact of observations on task completion. Different sensing actions may be beneficial but are limited by cost and effectiveness. The study focuses on collaborative grasping settings with a helper providing sensor readings to assist the actor in making grasp decisions. VOA is compared to established concepts like Value of Information (VOI) and Information Gain (IG). The research aims to optimize robotic grasping performance through informed decision-making based on observation benefits.
Analytical and data-driven approaches are used in robotic grasping research, with a focus on physical and dynamical models or labeled samples for policy ranking. VOA is formulated to estimate the influence of observations on successful grasps, adapting ideas from active perception and sensor planning in robotics. The study evaluates VOA in simulated and real-world settings, demonstrating its predictive capabilities for assisting robots in choosing optimal sensing actions.
The complexity analysis reveals that precalculating observation similarities can optimize VOA computation efficiency. Different belief update functions impact VOA effectiveness, with some metrics outperforming others due to sensitivity to noise or inaccuracies in predicted observations. Overall, VOA shows promise in enhancing robotic grasping performance through informed decision-making based on observation benefits.
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by Mohammad Mas... kl. arxiv.org 03-19-2024
https://arxiv.org/pdf/2310.14402.pdfDybere Forespørgsler