In the wave of technology in the 21st century, artificial intelligence (AI), with its outstanding data processing capabilities and advanced learning algorithms, is increasingly penetrating into various scientific fields and becoming a key driving force for technological development. Scientific instruments, as the "eyes" and "arms" in scientific research, are also undergoing an unprecedented profound transformation with the support of artificial intelligence.
1、Disruption of Laboratory Architecture: From "Human Dominance" to "Human Computer Symbiosis"
The future laboratory will no longer be a collection of cold equipment, but a collaborative network composed of embodied intelligent humanoid robots, autonomous scientific instruments, and human scientists. Inspired by the structure of human arm, Dr. Yang Kaisheng proposed a bionic robot arm in his paper "Research on the stiffness characteristics of modular rope driven bionic robot arm" published in Ningbo Institute of Industrial Technology, Chinese Academy of Sciences in 2020. The robot arm is composed of a three degree of freedom shoulder joint, a single degree of freedom elbow joint and a three degree of freedom wrist joint in series. Each joint module is a rope driven parallel mechanism. The moving platform and the fixed platform are connected by passive joints, and light ropes are used to transfer driving force instead of rigid bars. The drive units of the ropes are installed on the base of the robot arm. Therefore, modular rope driven biomimetic robotic arms have the characteristics of light weight, low moment of inertia, high load/self weight ratio, large workspace, variable stiffness, and high safety. They are particularly suitable for application scenarios where robots and humans collaborate, and have important research significance and application value.
The introduction of humanoid robot technology is driving a paradigm shift in the role of laboratory assistants. Robots equipped with multimodal perception systems can autonomously perform tasks such as sample handling, instrument calibration, and hazardous experimental operations. The dynamic grasping and fine operation capabilities demonstrated by the Yushu Technology G1 robot are powerful proof; After optimization in laboratory scenarios, it is expected to replace humans in high-risk operations such as weighing toxic chemicals and transferring radioactive samples.
On the other hand, the intelligence of laboratory infrastructure is another key dimension. By embedding AI modules into the experimental platform, ventilation system, and data acquisition equipment, the system can achieve autonomous optimization and dynamic adjustment of environmental parameters, and form an organic whole with humanoid robots through IoT technology. For example, abnormal instrument data can trigger the system to automatically dispatch robots for troubleshooting.
The above progress collectively drives the transformation of practitioners' functions: experimenters will be liberated from repetitive labor and focus on optimizing experimental design and interdisciplinary collaboration. Specifically, chemists can focus on molecular structure innovation, while robots are responsible for automated verification of synthetic pathways.
2. The 'Autonomous Evolution' of Scientific Instruments: From Tools to Intelligent Partners
Traditional scientific instruments are evolving into "research subjects" with autonomous decision-making capabilities, and the core of this transformation lies in the integration of AI big models and embodied intelligence.
At the level of instrument autonomous operation, devices such as scanning electron microscopes and mass spectrometers can analyze data in real-time and adjust parameters autonomously through built-in advanced algorithms. For example, multi-mode live imaging devices can automatically switch fluorescence labeling strategies based on recognized changes in the tumor microenvironment.
At the level of system collaboration, different instruments are interconnected through distributed AI systems to form "cluster intelligence". For example, in drug development, the system can command the mass spectrometer to complete component analysis, while calling AI models to predict molecular activity and automatically generate experimental reports.
This change has also redefined the role of instrument engineers, who are turning towards "AI trainers" and need to be proficient in algorithm tuning and multimodal data fusion. For example, optimizing mass spectrometer parameters no longer relies on experience, but dynamically matches the optimal conditions through reinforcement learning models.
3. Human computer collaboration mode: from "instruction execution" to "cognitive collaboration"
Humanoid robots are evolving from simple command execution terminals to intelligent partners with "scientific intuition".
At the interactive level, scientists can directly issue vague instructions such as "optimizing reaction yield" through voice or brain computer interfaces; Robots can use big language models to accurately parse intentions and autonomously schedule laboratory resources to complete tasks. Tesla Optimus' AI interaction technology has demonstrated such potential.
At the learning level, robots construct an exclusive "scientific research memory bank" by continuously learning experimental records and academic literature. For example, in materials science, it can automatically compare historical synthetic data and recommend crystal structure combinations that may have been overlooked for humans.
This poses a new requirement for scientists: they need to master "human-machine collaborative thinking" and deeply integrate their creative intuition with the powerful computing power of machines. For example, when biologists propose a hypothesis, robots can quickly generate thousands of gene editing schemes and simulate the results, and scientists can select the most groundbreaking research directions from them.
4. Ethics and Challenges: Hidden Concerns Behind Technological Leapfrogging
With the development of laboratory intelligence, data security and intellectual property protection are at the forefront. It is crucial to establish blockchain based encryption technology and a strict data permission grading system in the face of massive scientific research data that may be subject to malicious attacks or abuse.
Secondly, there is the risk of human skill degradation. Excessive reliance on automation systems may lead to young scientists losing basic experimental skills such as hands-on operation. Therefore, the future science education system needs to strengthen the "human-machine collaboration" course, and the robot assisted experimental design program offered by MIT is a leading example.
Finally, there is the ethical boundary dispute. If robots make significant scientific breakthroughs independently (such as discovering new drug formulations), how should the ownership of their achievements be defined? This is no longer something that a single institution can solve, and there is an urgent need for the international research community to work together to promote the reform of ethical frameworks.
5. Future vision: "Super Intelligent Laboratory" by 2035
Holographic interaction and virtual laboratories will bring revolutionary experiences: scientists can wear AR glasses to remotely control robots, observe molecular reactions in real time in virtual space, and even "feel" experimental processes directly through brain computer interfaces.
The instrument ecosystem will have the ability to self evolve: through federated learning technology, data from global laboratories can be securely shared, enabling algorithms to be continuously optimized. For example, when a new scanning mode is discovered in a certain location's nuclear magnetic resonance instrument, similar devices around the world can be upgraded synchronously.
Humanoid robots will develop a unique "scientific personality": advanced robots will possess specialized expertise in specific fields (such as "synthetic biology experts"), and they will be able to participate in academic conferences and even co write research papers on an equal footing with human scientists.
Conclusion
The transformation of future laboratories is not just a leap in technology, but also a collaborative evolution of human cognitive boundaries. When humanoid robots take on the role of the "second brain" in scientific research, scientists' creativity will break through traditional limitations and reach unprecedented heights. However, in this process, we must be vigilant about the risk of technological alienation and always ensure that the paradigm of "human-machine symbiosis" is firmly anchored to the ultimate goal of exploring truth. As Academician Qiao Hong emphasized, "The core value of humanoid robots is not to replace humans, but to expand their abilities." Only by adhering to this principle can science truly cross the threshold in the era of intelligence and sail towards the even more vast sea of stars.
