ChemGrasp: Affordance-Aware Dexterous Grasping for Laboratory Automation

Abstract The transition towards sustainable energy systems demands accelerated materials discovery. This requirement drives the development of fully automated chemical 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 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 evaluate execution feasibility in constrained simulated workspaces, ChemGrasp integrates a system-level motion planning pipeline featuring a phased hand execution strategy, enabling collision-free and kinematically reachable trajectories in simulation. 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 demonstrates a simulation-validated step toward adapting robotic dexterity to laboratory safety protocols for autonomous clean energy research.