Accelerating Discovery in Natural Science Laboratories with AI and Robotics: Perspectives and Challenges from the 2024 IEEE ICRA Workshop, Yokohama, Japan
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Abstract (arXiv)
Science laboratory automation enables accelerated discovery in life sciences and materials. However, it requires interdisciplinary collaboration to address challenges such as robust and flexible autonomy, reproducibility, throughput, standardization, the role of human scientists, and ethics. This article highlights these issues, reflecting perspectives from leading experts in laboratory automation across different disciplines of the natural sciences.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Jan 2025.
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Frameworkcited by 1
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- 2026SAFE-CHEM: Uncertainty-Aware Policy Switching for Robust Robotic Chemistrycited by 0
- 2026SAFE-CHEM: Uncertainty-Aware Policy Switching for Robust Robotic Chemistry10 Aug 2026
- 2026Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery6 May 2026
- 2025Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework1 Apr 2025