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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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May 20, 2026The Hidden Collapse: When INT4 Quantization Fails After Full ConvergencePost-training quantization has long operated under a deceptively simple premise. If a model converges to a low-loss solution in full precision, surely it occupies a region of weight space that will survive discretization May 20, 2026Translating Neural Silence into Visual Speech: How Generative Visual Grounding Unlocks EEG Understanding in Multimodal ModelsThe convergence of neuroscience and artificial intelligence has long promised to decode the electrical whispers of the brain into meaningful cognitive states. Yet the development of foundation models for electroencephalo May 20, 2026Corrective Retrieval Augmented Generation: Building Self Healing Knowledge Systems Introduction Large language models, despite their remarkable fluency, remain prone to hallucinations when tasked with factual precision. The root cause lies in their reliance on parametric knowledge encoded during train May 20, 2026The Compression Gap: How Discrete Action Tokenization Limits Scaling in Visuomotor ModelsA foundational assumption in Vision-Language-Action (VLA) research holds that upgrading the vision encoder should improve downstream manipulation performance. This principle appears robust in vision-language modeling, wh May 20, 2026Persistent Worlds from Pixels: How Lyra 2.0 Decouples Geometry from Imagination Introduction The convergence of video diffusion models and 3D reconstruction has initiated a significant shift in how we create virtual environments. Rather than constructing static meshes through laborious photogrammet May 19, 2026Teaching Vision-Language Models to Think in Mirrors: A Deep Dive into Geometric Logic ConsistencyVision-Language Models have achieved remarkable capabilities in recent years, demonstrating sophisticated multimodal understanding across domains ranging from medical imaging to autonomous navigation. Yet beneath this ve May 19, 2026Beyond Single-Step Prompts: Structured Planning for Long-Horizon Image EditingModern diffusion-based image editing models have reached an impressive threshold of photorealism and prompt adherence. Tools like InstructPix2Pix, Flux Kontext, and Qwen-Image-Edit execute concrete instructions with rema May 19, 2026Flow Sampling: Amortized Inference for Unnormalized Densities Through Denoising Objectives Introduction Sampling from unnormalized probability densities represents a foundational challenge across computational disciplines, from statistical physics to molecular dynamics and Bayesian inference. Unlike the stand May 19, 2026Variable Entropy Policy Optimization: Structural Constraints and Dynamic Calibration for Low-Resource Machine Translation Introduction The multilingual capabilities of large language models remain fundamentally uneven. While high-resource languages such as English and Mandarin exhibit robust performance across translation, reasoning, and i May 19, 2026From Masks to Pixels: Realigning Tampering Detection with Physical RealityThe question posed by Morpheus in The Matrix resonates with particular urgency as generative models produce imagery functionally indistinguishable from photography. In "From Masks to Pixels and Meaning: A New Taxonomy, B May 19, 2026The Geometry of Privacy: How a Fraction of Weights Dictate Membership Inference VulnerabilityThe persistent tension between model utility and privacy preservation in machine learning has traditionally forced practitioners into a binary choice. Either accept the vulnerability of membership inference attacks or sa May 19, 2026 May 19, 2026The Persistent Blind Spot: Why Audio Reasoning Remains Elusive for AI Introduction The recent trajectory of artificial intelligence suggests we have conquered question answering. Large language models now match or exceed human performance on text based benchmarks like Natural Questions, e May 19, 2026 May 19, 2026Rethinking the Default: Why Muon Deserves Attention in Tabular Deep LearningTabular deep learning occupies a peculiar position in the machine learning landscape. While computer vision and natural language processing have witnessed dramatic architectural innovations, supervised learning on tabula May 19, 2026Diffusion Models as Associative Memories: Rethinking the Memorization to Generalization Transition Introduction The tension between memorization and generalization stands as one of the central puzzles in modern machine learning. Large language models demonstrate remarkable capacity to recall factual information from May 18, 2026Architectural Transparency in Deep Reinforcement Learning: Moving Beyond Post-Hoc ExplainabilityDeep Reinforcement Learning has achieved superhuman performance in complex domains ranging from Atari games to robotic control. Yet this success masks a fundamental epistemological problem. The very representational powe May 18, 2026Engineering Causal Control: Sparse Carriers and the Reversibility of Fine-Tuned Behaviors in LLMsSupervised fine-tuning has become the dominant paradigm for instilling specific capabilities in large language models. Whether we are teaching a model to refuse harmful requests, adopt a particular writing style, or exec