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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 20, 2026Cross-Modal Consistency as Geometric Prior: Lessons from Omni123 on Mitigating Data Scarcity in 3D GenerationThe scarcity of high-quality 3D training data remains the fundamental bottleneck inhibiting the development of foundation models capable of native 3D generation and understanding. While the computer vision community has Apr 20, 2026Visual Attention Entropy as a Hallucination Signal: Beyond Inference-Time CorrectionsMultimodal large language models have made remarkable progress in understanding and generating responses about visual content, yet they remain plagued by hallucinations where models confidently generate text that lacks v Apr 19, 2026The Strategy Space of Synthetic Deception: Why Mixed-Truth Fake News Breaks Current Detectors Introduction The democratization of large language models has fundamentally altered the misinformation landscape. Commercial systems such as GPT-4 and Claude can now generate text of such fluency and apparent credibilit Apr 19, 2026Progressive Structural Reasoning: Disentangling Geometry and Motion in Monocular Articulated Reconstruction Introduction Reconstructing articulated three dimensional objects from a single image represents one of the more stubborn challenges in computer vision. Unlike rigid object reconstruction, where the task reduces to esti Apr 19, 2026When Reasoning Undermines Cooperation: Structural Mechanisms for Multi-Agent LLM Systems Introduction The dominant narrative in artificial intelligence safety assumes a positive correlation between capability and cooperation. As large language models become more sophisticated, exhibiting stronger reasoning Apr 19, 2026Learning the Unobservable: Generalized Actuator Networks and Sim-to-Real Transfer for Muscle-Actuated Robots Introduction In pharmacokinetic modeling, researchers face a fundamental constraint. They cannot measure drug concentration directly in every tissue, yet they must infer it from observable outputs such as blood plasma l Apr 19, 2026When Attention Deceives: Bayesian Solutions for Object Hallucinations in Vision Language Models Introduction Large Vision Language Models have achieved remarkable proficiency in describing visual content, yet they remain prone to object hallucinations, generating references to items absent from the input image. Th Apr 19, 2026 Apr 19, 2026From Feelings to Metrics: Formalizing the Structure of Informal LLM Evaluation The Benchmark Gap and the Rise of Vibe-Testing The evaluation of large language models has long been dominated by standardized benchmarks. Metrics such as accuracy on coding challenges, performance on academic exams, or Apr 19, 2026Staged Intelligence: The Developmental Biology of Post-Training in Nemotron-Cascade 2The pursuit of artificial general intelligence has long been dominated by the scaling hypothesis; the assumption that raw parameter count and computational mass are the primary determinants of capability. The recent rele Apr 19, 2026The Category Error at the Heart of Continual Reinforcement Learning Introduction The fundamental premise of classical reinforcement learning contains a hidden teleology. The agent seeks an optimal policy; once found, learning terminates. This framework, elegant and mathematically tracta Apr 19, 2026The Robot's Inner Critic: When Foundation Models Become Social Arbiters Introduction The generation of socially competent robot behavior has long been constrained by a fundamental tension between control and flexibility. Traditional approaches rely on hand-crafted motion libraries or rule-b Apr 19, 2026Adaptive Reasoning as Pareto Optimization: Lessons from AdaCoTChain of Thought reasoning has become the standard mechanism for enhancing Large Language Model performance on complex cognitive tasks. By encouraging models to articulate intermediate reasoning steps before generating f Apr 19, 2026Understanding the Stylistic DNA of Large Language Models: What Really Shapes How AI WritesThe quest to make AI-generated text indistinguishable from human writing has led to countless prompting strategies, temperature adjustments, and decoding tweaks. But new research suggests we might be focusing on the wron Apr 18, 2026Sketching Learning Algorithms: From Data Deletion to Causal AttributionHow do we know whether a specific image in a training set caused a generative model to produce a particular output? This question sits at the intersection of machine learning interpretability, privacy law, and causal inf Apr 18, 2026Decoupling Regression Capacity from Model Scale: A Technical Analysis of RELISHThe text to text paradigm has unified natural language processing under a single generative framework. Yet this unification comes with significant costs when applied to predictive tasks like regression. In their preprint Apr 18, 2026Shared State as Infrastructure: Why Personal AI Needs a New Systems Layer The Fragmentation Problem in Personal Computing Consider a seemingly simple question: "Is my heart rate too high right now?" Answering this situated query requires synthesizing data from disparate sources. Your workout Apr 18, 2026Beyond Static Knowledge: Retrieval-Augmented Segmentation for the Open World The Knowledge Cutoff Problem in Visual Understanding Multimodal large language models have achieved remarkable proficiency in reasoning segmentation tasks. Models such as LISA can parse complex queries like "segment the