ChemGrasp: Affordance-Aware Dexterous Grasping for Laboratory Automation

Clicks: 60
ID: 319593
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #12 of 52 articles by views in journal of modern power systems and clean energy

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
openalex_W7167272600 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xuanwei Liu, Tiewei Shang, Rui Wang, Junliang Li, Jisong Pan, Zixin Wang, Yongquan Chen
Are you one of these authors? Claim your profile
Journal journal of modern power systems and clean energy
Year 2026
DOI
10.1093/ce/zkag038
URL
https://doi.org/10.1093/ce/zkag038
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.