Using Robotics in Laboratory Decision Making

🔮 Are LLMs and reasoning enough to build self-driving instruments and laboratories? Autonomous scientists powered by LLMs and agentic reasoning have become a compelling narrative. But here’s the problem: laboratory autonomy is not primarily a text problem. Experiments don’t run in language - they run in the world of instrument-addressable actions, constraints, latencies, costs, and rewards. In our new paper based on the experience building autonomous SPMs, STEMs, nanoindentors, syntehis robots and connecting them in eamles workflows, we argue for a bottom-up foundation for self-driving laboratories (SDLs) that complements language-first planning. The core idea is simple: if you want an autonomous lab, you need frameworks that translate hypotheses into decisions executable on real tools - not just plans that read well. We propose three operational principles: 1. Optimize for information-efficiency, not just “more data.” Maximize decision-relevant information per unit experimental cost. Practically, that means a pairing of two instincts that sound contradictory but aren’t: - Comprehensive acquisition when it’s cheap and high-yield, and - Minimal decisive experimental design when you need to discriminate mechanisms or choose the next action. The goal is not “data abundance,” it’s agency - decision quality per dollar per hour. 2. Require hypotheses to compile into experimental footprints. A hypothesis is not complete until it has an experimental footprint: - the actions the instrument can actually take, - the constraints (safety, ranges, scheduling, sample state), - the observables the tool can measure, and - the decision rules that map outcomes to next steps. If it can’t be compiled into this footprint, it can’t be executed by an SDL - no matter how good the LLM prompt is. 3. Treat reward design as the central bottleneck. Closed-loop experimentation lives or dies by reward design - especially when the measurements are high-dimensional and the objective is not a single scalar “score.” Mapping images/spectra/multimodal streams to utility, balancing exploration vs exploitation, and extending to sequential decision-making is where most autonomy efforts quietly stall. Reward isn’t an afterthought; it’s the specification of the scientific intent. We also frame SDLs as what they really are: long-horizon, out-of-distribution discovery systems. If you care about durable technological impact (and not just lab-scale optimization demos), you have to accelerate the full experiment → model → decision loop while emphasizing physical and chemical knowledge discovery, not only short-term correlations. In other words: LLMs can help with planning and abstraction. But autonomy happens at the instrument boundary - where actions, costs, and rewards are real. Boris Slautin Yu Liu Timur Bazhirov Elham Foadian Gerd Duscher Mahshid Ahmadi https://lnkd.in/gHm-z9B9

Self-driving labs you can actually afford: Bayesian optimization meets $5,000 hardware Self-driving laboratories (SDLs) promise to revolutionize chemical discovery by closing the loop between experiment design, execution, and analysis. But there's a catch: most existing platforms cost upwards of $100,000—often much more once you add inline analytics like NMR or HPLC. That price tag has walled off autonomous experimentation to a handful of well-funded groups, amplifying the Matthew effect in chemical research. Simone Pilon and coauthors tackle this with RoboChem-Flex, a modular SDL built largely from 3D-printed parts, Arduino microcontrollers, and aluminum profiles, with a human-in-the-loop entry configuration that brings the total cost to around $5,000. But the more interesting story is the ML stack on top. At its core sits "RoBrains," a Bayesian optimization engine built on BoTorch supporting a broad toolkit: single- and multi-objective acquisition functions (UCB, qEHVI, qLogNEHVI, qLogNParEGO), GP and random forest surrogates, transfer learning via multi-task GPs, hybrid batching, and heteroskedastic noise modeling for low-SNR analytics. The team validates the platform across six case studies that each stress-test a different ML capability: an adaptive UCB that flips between exploitation and exploration when yields plateau (photocatalytic trifluoromethylation, 70% in 2 min); hypervolume optimization for selectivity trade-offs (deoxygenative C–H alkylation); noise-aware optimization compensating for a homemade Raman setup (H/D exchange, 64% D incorporation vs. 0–38% in prior reports); transfer learning across two Buchwald–Hartwig couplings using UMAP-projected DFT ligand descriptors, where the second substrate converged in just 4 extra experiments after learning from the first; and a three-objective enantioselective [2+2] cycloaddition (>99% ee, 80% yield). A recurring theme: featurization and acquisition function choice matter as much as the surrogate model, and modular frameworks let chemists match the algorithm to the problem rather than the other way around. For industrial R&D—pharma process chemistry, agrochemicals, catalyst discovery—this lowers two barriers at once: the capital cost of automation and the data-efficiency cost of exploring large condition spaces. Smaller teams can now run transfer-learning-driven catalyst screens or impurity-aware multi-objective optimizations without dedicated HTE, and the open-source code and hardware files mean methodology developed in academic labs can transfer directly into process development. Paper: Pilon et al., Nature Synthesis (2026) — CC BY 4.0 | https://lnkd.in/ewXEpqXz #MachineLearning #BayesianOptimization #SelfDrivingLabs #AI4Science #AutomatedChemistry #FlowChemistry #Photocatalysis #Biocatalysis #DrugDiscovery #ProcessChemistry #TransferLearning #OpenScience #LabAutomation #ChemInformatics #AIforChemistry

Some people talk about Self-Driving Labs as a future technology. I want to talk about what they actually do for biologists today. 🧬🤖 Before co-founding Trilobio, I spent years watching the same problems play out over and over. Brilliant scientists spending countless hours pipetting 96-well plates. Afternoons lost to transferring samples between instruments. Evenings spent checking on incubations that a timer and a protocol already predicted would be fine. Sound familiar? Manual execution can be inconsistent, error-prone, and exhausting. Full automation helps biologists run more experiments with improved consistency and accuracy, but it can be expensive and inflexible. At Trilobio, we’re building an affordable Self-Driving Lab that runs continuously and flexibly adapts to the data generated in your experiment. I recently wrote an article outlining the benefits of Self-Driving Labs. Here are a few: 1. Your experiments don't stop when you leave 🏠  Protocols run overnight and through weekends without someone standing at the bench. Executing research tasks—pipetting, plate transfers, timing—are handled end-to-end by the SDL. Critically, SDLs have robust error detection and fault tolerance to ensure long-term, high-quality execution. 2. Each run informs the next 🔀 SDLs are closed-loop, which means AI programmatically evaluates results and updates protocols as experiments progress. This accelerates discovery by compressing weeks of data analysis, decision making, and protocol redesign. 3. Experiments adapt when biological conditions change 🌡️ Biological systems change over time. Traditional automation executes against a fixed schedule and breaks down when conditions change. SDLs are biologically aware - they monitor conditions in real time and adjust accordingly. The labs adopting Self-Driving infrastructure *today* will have better data, true reproducibility, and faster iteration cycles *tomorrow*. You can read the full blog here: https://lnkd.in/gAeXNYEi #labautomation #SDL #selfdrivinglab

Ever seen a laboratory run experiments almost entirely on its own? What once depended on long hours at the bench is now being transformed by autonomous robot scientists. Imagine this: → Robotic systems performing molecular biology experiments with extreme precision → Every detail recorded, from pipette angles to mixing speeds → Failed experiments captured and analyzed, not discarded → AI models learning continuously and proposing smarter next steps This is not about replacing biologists. It’s about freeing them. Robotics in molecular biology reduces repetitive manual work, minimizes human error, and dramatically accelerates discovery. When combined with AI, these systems turn raw experimental data into meaningful insights, allowing scientists to focus on interpretation, strategy, and innovation. What’s powerful here is the shift in mindset. Think about modern research environments: ↳ How much time is lost on repetitive lab tasks? ↳ How much valuable data is never recorded because experiments “failed”? ↳ How much faster could discovery move with continuous, unbiased experimentation? Autonomous labs show that the future of science lies in collaboration between human expertise and intelligent machines. When robotics handles execution and AI handles learning, biologists can focus on what truly matters: asking better questions and creating real impact. —————————————— 𝗙𝗼𝗹𝗹𝗼𝘄 👉Muhammet Furkan Bolakar and 𝗮𝗰𝘁𝗶𝘃𝗮𝘁𝗲 𝘁𝗵𝗲 𝗯𝗲𝗹𝗹𝗹 🔔 for more updates on how #robotics, #automation and #science are shaping the future. Robot Technology: RoboSapienss Science Biology: Mr.Biyolog Digital Marketing: Bignite Digital —————————————— CTO ROBOTICS Media Onur Sezgin Florian Palatini Miloš Kučera Eduardo BANZATO Amir Sanatkar Amine BOUDER Christine Raibaldi Marcus Scholle Alexey Navolokin #biology #molecularbiology