a post with bibliography

The transition towards sustainable energy systems has catalyzed an urgent demand for accelerated materials discovery, driving a paradigm shift towards fully automated chemical unmanned laboratories. While multi-fingered dexterous hands offer the kinematic flexibility required to manipulate complex laboratory glassware, deploying them in safety-critical chemical environments remains a formidable challenge. Existing data-driven grasping models prioritize geometric stability but largely overlook strict functional constraints, often producing grasps that catastrophically occlude vessel openings and cause sample contamination. To address this critical bottleneck, we present ChemGrasp, an affordance-aware dexterous grasping framework tailored for laboratory automation. Our approach introduces a task- specific affordance module during the inference phase of a generative model, employing an energy-based optimization function to strictly penalize semantic violations. Furthermore, to ensure practical executability within constrained workspaces, ChemGrasp integrates a system-level motion planning pipeline featuring a phased hand execution strategy, guaranteeing collision-free and kinematically reachable trajectories. Extensive physics-based simulations demonstrate that ChemGrasp significantly elevates the Safe Success Rate (SSR) by eliminating functional violations, reliably executing dynamic grasp sequences in both floating and full-pipeline tabletop settings. Ultimately, this framework bridges the gap between unconstrained robotic dexterity and rigorous laboratory safety protocols, laying a robust foundation for next-generation autonomous clean energy research.