How to Test AI Robot Capabilities

IBM Research 𝗮𝗻𝗱 Yale University 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗳𝘂𝗹𝗹 360° 𝗿𝗲𝘃𝗶𝗲𝘄 𝘀𝗰𝗮𝗻 𝗼𝗻𝗲 𝗼𝗳 𝗵𝗼𝘄 𝘄𝗲 𝘁𝗲𝘀𝘁 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀. ⬇️ They looked at 120+ evaluation methods — and mapped out what’s working and what’s missing. Currently everyone’s building AI agents. Almost no one agrees on how to properly evaluate them. This is critical, because without rigorous evaluation, we can’t trust these systems to be reliable, safe, or ready for real-world use. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘀𝘁𝗮𝗻𝗱𝘀 𝗼𝘂𝘁: ⬇️ 1. 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀 ≠ 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 ➜ Agents aren’t static LLMs. They act, adapt, and evolve. Old-school metrics can’t keep up with real-world autonomy. 2. 𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗻𝗼𝘄 ➜ Benchmarks like LLF-Bench evaluate how agents process feedback and course-correct (which is crucial for evaulation quality). Without this, agents just repeat their mistakes. 3. 𝗖𝗼𝘀𝘁-𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗶𝘀 𝗯𝗲𝗶𝗻𝗴 𝗶𝗴𝗻𝗼𝗿𝗲𝗱 — 𝗱𝗮𝗻𝗴𝗲𝗿𝗼𝘂𝘀𝗹𝘆 ➜ Top agents burn insane tokens and API calls. We need benchmarks that track performance and price. Otherwise no one can afford to deploy them. 4. 𝗙𝗼𝘂𝗿 𝘀𝗸𝗶𝗹𝗹𝘀 𝗱𝗲𝗳𝗶𝗻𝗲 𝘁𝗼𝗽-𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀 ➜ It's critical to evaluate each individual component — otherwise, key weaknesses can go unnoticed and compromise the overall performance: * Breaking down complex tasks (planning) * Using tools and APIs (tool use) * Learning from feedback (reflection) * Remembering previous steps (memory) 5. 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 ➜ New benchmarks simulate actual jobs: * Online shopping (WebArena) * Debugging code (SWE-Bench) * Helping customers (τ-bench) * Research tasks (PaperBench) * Multi-step workflows (OSWorld, CRMWorld) More in the comments and below! 𝗪𝗮𝗻𝘁 𝗺𝗼𝗿𝗲 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻𝘀 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀? Subscribe to Human in the Loop — my new weekly deep dive on AI agents, real-world tools, and strategic insights: https://lnkd.in/dbf74Y9E

AI Evaluation Frameworks As AI systems evolve, one major challenge remains: how do we measure their performance accurately? This is where the concept of “AI Judges” comes in, from LLMs to autonomous agents and even humans. Here is how each type of judge works - 1. LLM-as-a-Judge - An LLM acts as an evaluator, comparing answers or outputs from different models and deciding which one is better. - It focuses on text-based reasoning and correctness - great for language tasks, but limited in scope. -Key Insight : LLMs can not run code or verify real-world outcomes. They are best suited for conversational or reasoning-based evaluations. 2. Agent-as-a-Judge - An autonomous agent takes evaluation to the next level. - It can execute code, perform tasks, measure accuracy, and assess efficiency, just like a real user or system would. -Key Insight : This allows for scalable, automated, and realistic testing, making it ideal for evaluating AI agents and workflows in action. 3. Human-as-a-Judge - Humans manually test and observe agents to determine which performs better. - They offer detailed and accurate assessments, but the process is slow and hard to scale. - Key Insight : While humans remain the gold standard for nuanced judgment, agent-based evaluation is emerging as the scalable replacement for repetitive testing. The future of AI evaluation is shifting - from static text comparisons (LLM) to dynamic, real-world testing (Agent). Humans will still guide the process, but AI agents will soon take over most of the judging work. If you are building or testing AI systems, start adopting Agent-as-a-Judge methods. They will help you evaluate performance faster, more accurately, and at scale.

Most software engineers think of testing as ensuring the code runs as expected. With AI? That’s only the beginning. AI isn’t just executing predefined instructions—it’s making decisions that impact real lives. In industries like healthcare, law enforcement, and finance, an AI system that “works” in a test environment can still fail catastrophically in the real world. Take Microsoft’s Tay chatbot from years ago as an example.  It wasn’t broken in a traditional sense—it just wasn’t tested against adversarial human behavior.  Within hours, it spiraled out of control, generating offensive content because the testing process didn’t account for real-world unpredictability. This is where traditional software testing falls short. ✔️ Unit testing ensures individual components function. ✔️ Integration testing checks if modules work together. ✔️ Performance testing evaluates speed & scalability. ✔️ Regression testing re-runs test cases on recent changes. But for AI, these checks aren’t enough. AI needs additional layers of validation: 🔹 Offline testing – Does the model work across multiple test cases and adapt to new data? 🔹 Edge case evaluation – Does it handle unexpected or adversarial inputs? 🔹 Scalability assessment – Can it maintain accuracy with growing datasets? 🔹 Bias & fairness testing – Does it make ethical decisions across groups? 🔹 Explainability checks – Can you understand how it reached a decision? (Critical in specific applications.) 🔹 Post-deployment testing – Can it maintain accuracy after deployment? I’ve seen companies launch AI tools in a matter of weeks—only to shut them down a few months later due to complaints or embarrassing failures—all due to a lack of AI testing. If your AI tool passes software functionality checks but fails on quality, scalability, and adaptability, it's time to peel back the layers. AI tools shouldn't just “run.” They need to work reliably in the real world over prolonged periods of time.

We talk a lot about humanoid robots learning to walk, lift, balance, and adapt. But the real breakthrough isn’t just making robots more capable — it’s making their performance measurable, repeatable, and trusted. That’s why the work happening right now at Iowa State University matters. Dr. Bowen Weng and his team are developing real-world test methods for legged and humanoid robots — evaluating how they respond to falls, impacts, uneven ground, and system disturbances. Not to create “super robots,” but to build the evidence base needed for safe, predictable deployment. This research is feeding directly into ASTM F45.06 Legged Robots, where standardized performance evaluations are being shaped for the first time. Because before humanoids show up in warehouses, hospitals, or construction sites, we need to be able to answer one basic question: Can we trust how they move? This is Research-to-Standards in motion: ➡️ Lab data → repeatable evaluation methods ➡️ Evaluation methods → shared benchmarks ➡️ Shared benchmarks → safer deployments The humanoid era won’t be defined by who has the fanciest demo. It will be defined by who can prove reliability, safety, and stability at scale. And that requires standards.