Shiza Charania
33 followers · 39 following · 2382 views
on the atlas — 75
- Introducing S1: In-Context Learning for Robotics | Skild AI1 savers
- Line Switching2 savers
- Previewing the Model Hardware Standard \ Anthropic10 savers
- Mean People Fail6 savers
- Paul Buchheit: The most important thing to understand about new products and startups4 savers
- Smooth Operator: A Real-Time Sampling-Based Algorithm for Kinematic Hand Retargeting1 savers
- GelNeuro: A Sensing-Computing Integrated Neuromorphic Tactile System for Texture Recognition1 savers
- The Hardest Lessons for Startups to Learn3 savers
- America Spins on Westmag - Not Boring by Packy McCormick1 savers
- Cultivating a state of mind where new ideas are born65 savers
- Nathan Lepora – Professor of Robotics & AI | University of Bristol1 savers
- To be alive is to live wanting something - Sherry Ning1 savers
- What Do Robotics Leaderboards Tell Us About The State of Robot Learning?1 savers
- How China's Unitree Will Dominate Global Robotics11 savers
- 1X World Model | From Video to Action: A New Way Robots Learn1 savers
- hamr / 3D_hapkit · GitLab1 savers
- Neoprene Rubber Tubing - Petroleum Resistant Tubing | Custom Advanced1 savers
- CHARM LAB ME327/Home Page1 savers
- Jet_mill1 savers
- LeFlexiTac Docs1 savers
- MMintLab/hydroshear: High Fidelity Tactile Shear Simulation ·1 savers
- FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning1 savers
- FORTE: Tactile Force and Slip Sensing on Compliant Fingers for Delicate Manipulation1 savers
- An Opinionated Guide to ML Research52 savers
- Generalist - GEN-1: Scaling Embodied Foundation Models to Mastery3 savers
- Corgi Cafe — San Francisco's First 24/7 Cafe5 savers
- Resentment is needing others to change so you can stay the same12 savers
- SayCan: Grounding Language in Robotic Affordances1 savers
- World Models: Computing the Uncomputable3 savers
- Electric motor scaling laws and inertia in robot actuators | Robot Daycare6 savers
- RL Environments and RL for Science: Data Foundries and Multi-Agent Architectures2 savers
- TRELLIS.2: Native and Compact Structured Latents for 3D Generation2 savers
- Causal Video Models Are Data-Efficient Robot Policy Learners | Rhoda AI4 savers
- Activation Steering in 2026: A Practitioner's Field Guide | Subhadip Mitra1 savers
- FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning1 savers
- RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies1 savers
- How do We Quantify Progress in Robotics? - by Chris Paxton1 savers
- Steerable Vision-Language-Action Policies for Embodied Reasoning and Hierarchical Control1 savers
- RI Seminar: Jitendra Malik : Robot Learning, With Inspiration From Child Development - YouTube2 savers
- Anirudha Majumdar on X: "Should we predict pixels for world models in robotics?" / X1 savers
- PLATO Hand: Shaping Contact Behavior with Fingernails for Precise Manipulation1 savers
- Humanity's Last Machine26 savers
- An open letter to 56 people I know - by Sasha Chapin20 savers
- Childhoods of exceptional people - by Henrik Karlsson69 savers
- Things You Learn Dating Cate Hall - by Sasha Chapin52 savers
- Intentionally Making Close Friends — Neel Nanda38 savers
- IEEE Xplore Full-Text PDF:36 savers
- The First Fully General Computer Action Model | blog35 savers
- reflections on palantir - Nabeel S. Qureshi35 savers
- Salary Negotiation: Make More Money, Be More Valued | Kalzumeus Software34 savers
- 101 things I would tell my self from 10 years ago28 savers
- The friendship problem - by Rosie Spinks25 savers
- 50 things I know - by Cate Hall - Useful Fictions24 savers
- OpenAI Email Archives (from Musk v. Altman) — LessWrong20 savers
- 50 things I know - by Sasha Chapin - Sasha's 'Newsletter'19 savers
- How To Understand Things - Nabeel S. Qureshi18 savers
- Choose Good Quests – Founders Fund18 savers
- Building the heap: racking 30 petabytes of hard drives for pretraining | blog17 savers
- The Tyranny of Stuctureless16 savers
- Hugging the X-Axis - David Perell15 savers
- “Build What’s Fundable” - by Kyle Harrison - Investing 10111 savers
- The Case Against Travel | The New Yorker10 savers
- EMERSON - ESSAYS - SELF-RELIANCE9 savers
- Familiarity and Belonging - by Simon Sarris9 savers
- tell your friends you love them9 savers
- Sporks of AGI - by Sergey Levine - Learning and Control8 savers
- it's okay to care too much - by Ava - bookbear express8 savers
- Advice That Actually Worked For Me - Nabeel S. Qureshi7 savers
- State of Robot Learning, December 20257 savers
- Nominative determinism - Wikipedia6 savers
- Mini Blog Post 15: The illusion of doing nothing — Neel Nanda6 savers
- AI: Markets for Lemons, and the Great Logging Off3 savers
- Conduit3 savers
- A Student's Guide to Startups3 savers
- Robot Learning: A Tutorial - a Hugging Face Space by lerobot2 savers
highlights — 148
You have to be the right kind of determined, though. I carefully chose the word determined rather than stubborn, because stubbornness is a disastrous quality in a startup. You have to be determined, but flexible, like a running back. A successful running back doesn't just put his head down and try to run through people. He improvises: if someone appears in front of him, he runs around them; if someone tries to grab him, he spins out of their grip; he'll even run in the wrong direction briefly if that will help. The one thing he'll never do is stand still. [7]
The Hardest Lessons for Startups to Learnbenchmarking our initial SKUs against what they need in order to get larger offtake agreements against an aggressive manufacturing ramp, which justify making larger buys and investments of input materials
America Spins on Westmag - Not Boring by Packy McCormickThe design-cycle time for actuators right now, for almost anyone in the US, is in the months, because you rely on overseas suppliers to order pretty much everything.
America Spins on Westmag - Not Boring by Packy McCormickStack those tolerances on top of each other (lamination, winding, magnet orientation, rotor balance, bearing fit, etc…) and you start to see why motor manufacturing is mostly a process problem, not a materials problem. The materials are simple. The process is annoyingly precise.
America Spins on Westmag - Not Boring by Packy McCormickThe truest driver of a decision is not whether it makes sense but whether it makes you more: more awake, more free, more alive, as if a magnetic pole has been planted somewhere in the earth just to make your heart expand the closer you are to it.
To be alive is to live wanting something - Sherry NingThe tactile map drifts with temperature, so recalibrate if the sensor has been powered on for more than 30 minutes.
LeFlexiTac Docspolicy to focus more on force input during initial training phases and gradually balances force with visual inputs as training progresses
FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learningdisregard irrelevant visual details once contact is established and rely solely on force feedback to perform tasks such as lifting a box or rolling dough
FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learningcontact force signals are typically less discriminative, often remaining near zero for extended periods when the arm is not in contact with the environment during an episode
FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learningcreates a random grasping force against the load cell, which we compare against the FORTE readings
FORTE: Tactile Force and Slip Sensing on Compliant Fingers for Delicate ManipulationTo characterize how well we measure grasp force across different shapes, we perform multiple grasps on custom load-cell testing indentors
FORTE: Tactile Force and Slip Sensing on Compliant Fingers for Delicate Manipulationenvironment companies hire overseas developers to replicate the UI of sites like DoorDash or Uber Eats and sell the mockups to labs
RL Environments and RL for Science: Data Foundries and Multi-Agent ArchitecturesScaling up RL is difficult as it requires a steady stream of tasks the model needs to solve and learn from.
RL Environments and RL for Science: Data Foundries and Multi-Agent ArchitecturesBut OpenAI’s progress last year and ability to keep up using an older base model was existence proof of the efficacy of post-training.
RL Environments and RL for Science: Data Foundries and Multi-Agent ArchitecturesFigure 2:Pipeline for extracting qualitative policy characteristics from RoboArena’s rich evaluation data. We use a VLMs to categorize scenes and tasks, and then use an LLM to aggregate information across a large number of evaluation rollouts into a policy report that summarizes qualitative strengths and weaknesses, and cites concrete evaluation rollout videos as evidence.
RoboArena: Distributed Real-World Evaluation of Generalist Robot PoliciesThese are likely to expand, although major companies right now have too much to lose and too little to gain to bother competing.
How do We Quantify Progress in Robotics? - by Chris PaxtonInference-time scaling with verifiers. Video models allow VLMs to be used directly as verifiers. By generating multiple videos and scoring them with a VLM, we can filter out unrealistic or low-quality generations. This provides a simple recipe for inference-time scaling [14].
Anirudha Majumdar on X: "Should we predict pixels for world models in robotics?" / Xhallucinations: objects that duplicate, appear from nowhere (see example below from our Veo work), disappear, or morph either spontaneously or when re-appearing after an occlusion.
Anirudha Majumdar on X: "Should we predict pixels for world models in robotics?" / Xurrent video models in robotics struggle to produce high-quality generations beyond 20-30 seconds at most.
Anirudha Majumdar on X: "Should we predict pixels for world models in robotics?" / X(1) generating a video of imagined success and using an inverse dynamics model to infer the requisite actions [1, 2, 3], or (2) by directly optimizing plans using an action-conditioned world model [4, 5].
Anirudha Majumdar on X: "Should we predict pixels for world models in robotics?" / XPushing farther from the center of rotation produces more torque for the same magnetic force
Humanity's Last Machinethe motor must flip its magnetic poles at precisely the right moment
Humanity's Last Machinenotoriously difficult to join to metal frames without creating weak points
Humanity's Last MachineChina produces 85-90% of global magnesium
Humanity's Last Machineprocessing magnesium in a semi-solid state (never fully liquid)
Humanity's Last Machinehumanoid limbs where every gram saved reduces motor workload and power consumption
Humanity's Last Machinehigh-wear areas like shafts, gears, pins, fasteners, and bearings
Humanity's Last Machinemost reliable material for building structural housings
Humanity's Last MachineThe Inverse Dynamics Model (IDM): We bridge pixels to actuators by training an IDM to predict the exact action sequence required to transition between the model's generated frames. We use the IDM’s metrics and rejection sampling to enforce kinematic correctness of generations.
1X World Model | From Video to Action: A New Way Robots LearnProprioception and actions need to be inferred from the SLAM estimate of end effector pose Camera images all feature human arms holding a device, but at inference time the robot sees robot arms instead
State of Robot Learning, December 2025To the public intellectual, one must ask: if your ideas are so good, why aren’t you executing on them? It is much easier (and less impactful) to write about the importance of “green tech” than to build Tesla. Moreover, many self-titled public intellectuals are not even particularly intellectual. They are just… public.
Choose Good Quests – Founders FundBesides that fundamental architecture, we also have an energy recycling architecture involving our muscles and tendons. We store energy in our tendons and reuse it on the next step — our Achilles tendon at the back of each of our lower legs is the one that stores most energy and the one most likely to rupture.
Why Today’s Humanoids Won’t Learn Dexterity – Rodney BrooksIf the big tech companies and the VCs throwing their money at large scale humanoid training spent only 20% as much but gave it all to university researchers I tend to think they would get closer to their goals more quickly.
Why Today’s Humanoids Won’t Learn Dexterity – Rodney BrooksIn the first video the person picks a match out of a box and lights it. The task takes seven seconds. In the second video the same person tries again but this time the tips of her fingers have been anesthetized so she no longer has any sense of touch right at her fingertips.
Why Today’s Humanoids Won’t Learn Dexterity – Rodney BrooksModern Vision-Language-Action models (VLAs) are actually quite bad at handling tasks which require even a very short memory.
Are Humanoid Robots Ready for the Real World?They have no mechanism to learn or remember the dryer settings I prefer for different clothes, and no mechanism to know which clothes I prefer to hang-dry. The answer to these questions is not as simple as read the label.
The Current Crop of AI Startups is not Prepared for Big WorldsIf you can train yourself to ask “is there a better way to do this?” at random intervals ten times a day, you will become unstoppable.
50 things I know - by Cate Hall - Useful Fictions“Are you in venting mode or solutions mode?”
50 things I know - by Cate Hall - Useful Fictionswhen we train our robotic foundation model on, for example, human data, and then present it with a new problem, it will try to predict how a human will approach this problem, rather than predicting an effective strategy for a robot
Sporks of AGI - by Sergey Levine - Learning and Control“so muchness” of modern life has given us commitment anxiety
Hugging the X-Axis - David Perellcombination of rapid or precise movements, adjusting motions based on subtle tactile feedback, and manipulating small, fragile, or deformable objects
Robot Dexterity Still Seems Hard - by Brian Potterthe older you get, the more expensive your time becomes
loneliness - by Elaine - manners & mysteryAnd because DOGE is constrained by a preexisting set of rules around reductions in force, the people DOGE is firing are disproportionately the more talented and more useful people
50 Thoughts on DOGE - by Santi Ruiz - Statecraftnumber of federal employees and dollars saved
50 Thoughts on DOGE - by Santi Ruiz - Statecraftmaking customers feel special for owning something rare
LululemonCIA has declassified extensive documents describing even more profound psychic abilities, like the ability to write on pieces of paper that are sealed in envelopes, or ‘remote view’ distant regions of space and time by just sitting there and letting your mind wander without even knowing the space-time coordinates you’re supposed to view.
Predictions for a Science Fiction Future - by Andrew CotePart of this awareness, of what I can actually do with several days of focused work and intention, is the pain of knowing how much I am not doing that anymore.
my phone is making me dumb - by Isabel - Mind Mine“I’m going to schedule a phone call with you in two months to catch up, I’ll send you the invite — if we need to adjust when we get closer to the date, that’s fine.”
50 things I know - by Sasha Chapin - Sasha's 'Newsletter'I know that it feels horrible to create from a place of defense
50 things I know - by Sasha Chapin - Sasha's 'Newsletter'she accepts uncertainty as a universal phenomenon.
Things You Learn Dating Cate Hall - by Sasha Chapin