Granted Robotics: Permission Before Motion

Inspiration A cup moves. A grasp slips. A successful demo no longer tells us what happens next. Granted Robotics makes that missing recovery evidence visible. What it does A hosted workbench for robotics integrators: inspect a two-arm table-setting plan, inject failures, execute real SO101 MuJoCo simulation, compare recovery on and off with the same seed, and export results. Guest access works immediately; Firebase accounts are optional. How we built it Shivam Gupta built Granted with AI assistance using React, TypeScript, FastAPI, MuJoCo and OpenVINO. A constrained OpenAI planner validates language against an actual rendered camera frame. A small imitation policy trained on 480 examples, with 120 held out, proposes joint targets through OpenVINO. Numerical correction remains mandatory. Firebase hosts the interface and Cloud Run executes simulation. Challenges we ran into A convincing animation is not physics evidence. We separated the illustrative browser view from real execution, camera input, measurements and downloadable traces. The prototype uses assisted grasps and ground-truth poses. It is not a certified safety system or an end-to-end VLA. Intel Core Ultra access is pending review; hardware validation is incomplete. Accomplishments that we're proud of 50 simulations across 10 randomized seeds: grip-loss recovery completed 10/10, versus 0/10 without recovery. Mean cup error was 14.05 versus 160.96 mm. Learned initialization reduced numerical iterations 5.3% in paired trials. All 29 regression tests and five deployed API checks passed. What we learned Recovery needs reproducible evidence, not just a success rate. What's next Our first customer hypothesis is an integrator turning recurring failures into regression tests. A paid pilot would measure diagnosis time saved. Next: broader tasks, end-to-end VLA control and Core Ultra validation. Pricing and demand remain hypotheses. Coherent evidence is the product.