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Notes on teaching robots new tricks

How the studio works, why tiny robots come first, what the checks catch, and how a policy gets from simulation to a servo.

workflowopen-duck-miniSep 29, 2026 · 6 min readFrom a sentence to a walking duck: how kualia turns a plain-language brief into a training taskA walk through the whole kualia workflow, from a one-sentence brief to an ONNX policy, using the Open Duck Mini and a small ramp.Read the post

Sep 27, 2026 · 5 minWhy we start with tiny robotsDesk-sized robots train on CPU in minutes to hours, cost little to own and cross the sim-to-real gap without heroics. That is why kualia's shelf starts with them.

Sep 25, 2026 · 6 min13 checks before a single training stepalia writes the training task, but it does not get to grade its own work. Every task passes 13 sandbox checks before training can start. Here is what each one catches.

Sep 23, 2026 · 5 minFrom simulation to servo: running your ONNX policy on a real SO-101What is inside a kualia ONNX export, how to read its metadata, a minimal onnxruntime loop for the SO-101, and the sim-to-real checklist before you power the servos.

Sep 21, 2026 · 5 minLet your coding agent train the robotkualia is a remote MCP server. Point Claude Code, Cursor or Codex at it and your agent can pick a robot, build the task, train it and fetch the ONNX policy from your editor.

Research digest

Short reads on new robotics and AI papers, written by our research assistant and reviewed before publishing. 1,060 so far.

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Apr 30, 2026Learning to Parallelize: How DistilBERT is Reshaping Automatic Loop Parallelization Introduction The multicore revolution has created a persistent software gap. While modern processors offer increasing parallelism through additional cores, exploiting that parallelism remains a manual and error-prone ta Apr 30, 2026Reconstructing the Unseen Majority: Long-Tail Internet Photo Reconstruction Introduction The democratization of photography has created an unprecedented archive of visual data online. Yet this vast repository follows a starkly unequal distribution. While iconic landmarks like the Colosseum or N Apr 30, 2026Tool-Augmented Reasoning in Radiology: How Reinforcement Learning Enables Transparent CT Interpretation Introduction Medical imaging has entered an era where vision-language models (VLMs) generate radiological reports with increasing fluency. Yet this capability masks a fundamental tension in clinical deployment. While mo Apr 30, 2026MolmoWeb and the Case for Vision-Centric Open Web AgentsThe pursuit of autonomous web agents has long been bifurcated between proprietary, closed systems and fragmented academic research lacking reproducible artifacts. While frontier models now demonstrate browser use capabil Apr 30, 2026Detecting Implicit Reward Hacking Through Gradient Fingerprints: A Technical AnalysisReinforcement learning with verifiable rewards (RLVR) has emerged as the dominant paradigm for enhancing reasoning capabilities in large language models. By optimizing for outcome level rewards, such as passing unit test Apr 30, 2026Stabilizing Diffusion Guidance with Adaptive Moments: When Optimization Meets Generative SamplingDiffusion models have established themselves as the dominant paradigm in generative modeling, powering applications from text-to-image synthesis to molecular design. Yet conditioning these models on external information Apr 30, 2026WildWorld: Bridging the Gap Between Video Generation and State-Aware World Modeling The Latent State Problem in Generative Video Recent advances in video generation have produced increasingly photorealistic sequences, yet a fundamental tension remains between passive video synthesis and active world mo Apr 30, 2026Certifiable Safety in Human-Robot Collaboration: Bridging Vision and Uncertainty QuantificationThe transition of industrial robotics from caged automation to collaborative workspaces represents one of the most significant shifts in manufacturing and service robotics. As autonomous systems enter human centered envi Apr 29, 2026Teaching Machines to Say "I Don't Know": A Targeted Approach to Hallucination ReductionLarge language models have achieved remarkable fluency, yet they remain fundamentally unreliable in one critical dimension. They hallucinate. They confabulate. They generate plausible sounding falsehoods with unearned co Apr 29, 2026ActionParty and the Challenge of Multi-Subject Control in Generative World ModelsThe transition from static content generation to interactive simulation represents one of the most significant shifts in contemporary artificial intelligence research. Recent advances in video diffusion models have enabl Apr 29, 2026Bridging the Embodiment Gap: How UniT Creates a Universal Language for Humanoid Control Introduction The pursuit of foundation models for humanoid robotics faces a fundamental data bottleneck. While large language models train on trillions of tokens scraped from the internet, and vision models absorb billi Apr 29, 2026Convergence Rates for the Adam Optimizer: When Adaptive Methods Follow Unexpected Trajectories Introduction The Adam optimizer, introduced by Kingma and Ba in 2014, has become the default workhorse for training deep neural networks across virtually every domain of artificial intelligence. Its combination of momen Apr 29, 2026 Apr 29, 2026When Reasoning Breaks Down: The Hidden Cost of Dialogue for Large Language Models The Evaluation Gap Between Benchmarks and Reality Modern Large Language Models demonstrate impressive performance on structured reasoning benchmarks, solving mathematical proofs, spatial puzzles, and temporal logic prob Apr 29, 2026 Apr 29, 2026YC-Bench and the Fragility of Long-Horizon AgencyLong-horizon planning remains the final frontier for large language model agents. While current systems excel at discrete tasks such as code generation or single-turn question answering, the ability to maintain strategic Apr 29, 2026Architectural Constraints for Reliable Reasoning: Analyzing the Box Maze FrameworkThe persistent vulnerability of large language models to adversarial manipulation has exposed fundamental limitations in current safety methodologies. While reinforcement learning from human feedback (RLHF) has become th Apr 29, 2026Probabilistic Bias Correction: Machine Learning as a Post-Processing Layer for Subseasonal Weather Prediction Introduction The subseasonal forecasting horizon, spanning 2 to 6 weeks into the future, has long represented a stubborn predictability barrier in atmospheric science. This temporal window is critically important for ag