[Paper Notes] DexJoCo-X: Benchmarking Action Representations for Multi-Hand Dexterous Manipulation
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Blog posts
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2026
SysID: Solving the Hidden Dynamics Between a Policy and a Dexterous Hand
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A policy command is only a request; system identification is what lets simulation learn how a dexterous hand actually responds.
[Project Notes] RL in a World Model, in Rust: What Our Pushing Experiments Taught Us
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A small Rust pushing task exposed a large gap: 82% success inside a learned model, 0% in the simulator. Notes on rollout error, cold-start RL, and a strong imitation baseline.
[Book Notes] Michel Foucault: The Panopticon and the Habits of Discipline
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Reading Foucault’s Discipline and Punish through Bentham’s Panopticon: uncertain observation, self-discipline, normalization, and the limits of applying this model to workplaces, schools, and AI monitoring.
Four Stages of Embodied Model Training: From Semantic Priors to Robot Deployment
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A bilingual framework for comparing VLA, world-action, and native sensorimotor models—with an interactive map of training stages and the human-to-robot data continuum.
[Project Notes] TwinDEX: Scaling Dexterous Manipulation with Twinned Hardware
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TwinDEX co-designs a wearable exoskeleton and a matching robotic hand so that robot-free demonstrations preserve kinematics, contact, appearance, sensing, and timing at deployment.
[Book Notes] Jeff Hawkins: A Thousand Brains and Intelligence as Many Models in Motion
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A Thousand Brains reframes intelligence as sensorimotor world-modeling carried out by many cortical modules, with consequences for neuroscience, robotics, AI, and the risks created by human belief.
[Book Notes] John von Neumann: The Statistical Language of the Brain
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The Computer and the Brain asks how slow, noisy, low-precision neurons produce fast and reliable intelligence—and why the brain’s language may differ fundamentally from mathematical notation.
[Book Notes] Douglas Hofstadter: Strange Loops, Meaning, and the Emergence of Mind
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Gödel, Escher, Bach explores how meaning, intelligence, and the self can emerge from formal rules through recursion, layered description, and strange loops.
[Book Notes] David Deutsch: Good Explanations and the Beginning of Infinite Progress
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David Deutsch’s philosophy of good explanations connects knowledge, fallibility, universal computation, open institutions, and the possibility of unbounded progress.
[Book Notes] James P. Carse: Finite Games, Infinite Play, and the Art of Keeping Possibility Open
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James P. Carse’s distinction between finite and infinite games offers a lens for understanding competition, identity, education, culture, technology, and the open-ended work of research and AI.
Why On-Policy Data Matters in Post-Training
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[Paper Notes] Human Universal Grasping
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Before the Robot GPT-3.5 Moment: Latent Actions, World Models, and Embodied Memory
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A reflective essay on latent actions, world models, KV-cache memory, and why robotics may still be before its GPT-3.5 moment.
A Practical Map of LLM Training Ecosystems
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A compact field note on how Megatron, Hugging Face, PyTorch, vLLM, SGLang, and verl fit together in a practical LLM training pipeline.
Force Control and the Missing Layer in Embodied Intelligence
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A late-night conversation over grilled skewers revealed a gap: most embodied AI researchers don’t think about what the robot arm is actually doing at the control level — and closing that gap might reshape how we think about action spaces.
Bootstrapping and the Game of Entrepreneurship
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Bootstrapping is the same pattern across compilers, AI, and startups: borrow external structure to cold-start, then recursively replace dependencies until the system sustains itself.
[Book Notes] Michael Polanyi: Tacit Knowledge, Personal Knowing, and What AI Still Cannot Tell
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Polanyi’s framework of tacit knowledge and personal knowing offers a lens for understanding what AI still cannot do — and why embodied, context-dependent skill remains hard to formalize.
Distilling Persons into Agents: A Survey of Recent ‘Person-as-Skill’ Projects
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A survey of projects that compress human personas into AI agent skills — from departed colleagues to public figures — and what this trend reveals about AI, memory, and identity.
[Project Notes] Robotic World Model Lite
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[Project Notes] PAL Robotics in mjlab
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The Singularity is Near
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Autonomous agents are now cheap and networked enough to spread faster than any institution can contain — the question is what order emerges after control becomes partial.
How to Align Teleoperation Devices with Robot End Effectors
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The hardest part of teleoperation is aligning coordinate frames between input devices and robot end effectors — a practical walkthrough.
Inverse Kinematics for Any URDF: Tasks, Weights, and Solver Design
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A practical guide to formulating IK as weighted nonlinear least-squares, with task definition, damping, and solver design that works for any URDF.
I Vibe Coded an Operating System
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I built a scripting-first OS prototype in a weekend with Codex, where the system language gradually takes over its own environment from the host.
Intelligence Is Not Only Reasoning
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Intelligence depends on retrieval and communication as much as reasoning — a powerful model limited by information gaps is still limited.
Cognitive Bandwidth in the AI Agent Era
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As AI tools get more capable, the bottleneck shifts from tool friction to human cognitive bandwidth — compressing intent and steering effectively becomes the key skill.
Updating website
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A major site update done mostly by Codex — page refactoring, content restructuring, and bilingual support.
2024
[Paper Notes] SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering - RSS 2023
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Key information
- Model-based reinforcement learning (MBRL) is potentially more sample-efficient than model-free RL.
- Integration of differentiable physics-based simulation and rendering facilitates the learning process.
- Pipeline: Real2Sim -> Learn@Sim -> Sim2Real to produce efficient policies.
[Paper Notes] DayDreamer: World Models for Physical Robot Learning - CoRL 2022
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Key information
- This paper learns two models: a world model trained on off-policy sequences through supervised learning, and an actor-critic model to learn behaviors from trajectories predicted by the learned model.
- The data collection and learning updates are decoupled, enabling fast training without waiting for the environment. A learner thread continuously trains the world model and actor-critic behavior, while an actor thread in parallel computes actions for environment interaction.