Control a robot arm through layered choices

01 · Role in the system

What Jev does here

Three layers narrow the decision: Jev selects an intent, then a contact and motion family informed by that choice, then one of 27 inputs covering Cartesian translations, wrist rotations, gripper open and close, and hold. Before an input executes, reversible simulator branches preview candidate effects locally, so each choice is made against predicted physics rather than hope. Everything is inspectable: model requests, predictions, controls, simulator states, costs, and trajectory media save together, and an interactive Decision Lab replays every run with Jev's choices and probabilities alongside the video. Three task configurations share one engine - closing a microwave in 14 decisions over 111 environment steps, a drawer in 20 over 155, and grasping and lowering a soup can in 40 over 314 - with task measurement, features, contact rules, and per-layer inputs configurable in JSON.

02 · Control boundary

Where Jev sits

Layered choices over a fixed 27-input space with local reversible physics previews before execution; every run persists request, control, state, and media for replay.

Code owns the loop, permissions, thresholds, validation, and side effects. Jev owns only the bounded judgments described above.

03 · Known limits

What this evidence does not prove

• Simulation only: LIBERO benchmark tasks, not physical hardware; the videos follow simulation time with waiting omitted.
• Three curated task configurations with chosen seeds, not a benchmark leaderboard claim.
• Success counts are the author's recorded runs, replayable from the shipped records.

04 · Attribution

Public sources

This is a Community record: the project was published by a third-party community author.

• Dimweaker ↗Community · github · public · checked 2026-09-23
• Dimweaker ↗Community · demo · public · checked 2026-09-23